Package: caffe-cuda
Description-md5: b93e8b129977d3d95bfdb17281d84865
Description-en: Fast, open framework for Deep Learning (Meta)
 Caffe is a deep learning framework made with expression, speed,
 and modularity in mind. It is developed by the Berkeley AI Research
 Lab (BAIR) and community contributors.
 .
 This metapackage pulls CUDA version of caffe:
  * caffe-tools-cuda
  * libcaffe-cuda*
  * python3-caffe-cuda
 And suggests these packages:
  * libcaffe-cuda-dev
  * caffe-doc
 .
 Note, this CUDA version cannot co-exist with the CPU_ONLY version.

Package: caffe-tools-cuda
Description-md5: 95a0dcba6d8fcf9105aa7d2691cba834
Description-en: Tools for fast, open framework for Deep Learning (CUDA)
 Caffe is a deep learning framework made with expression, speed,
 and modularity in mind. It is developed by the Berkeley AI Research
 Lab (BAIR) and community contributors.
 .
 It contains caffe executables, configured with CUDA.
 .
 Issue this command at the root of caffe source tree for a unit test:
   $ caffe-gtest --gtest_shuffle

Package: libaccinj64-10.0
Description-md5: 404daeb74b9a29243e00b395f84c5ada
Description-en: NVIDIA ACCINJ Library (64-bit)
 The Compute Unified Device Architecture (CUDA) enables NVIDIA
 graphics processing units (GPUs) to be used for massively parallel
 general purpose computation.
 .
 ACCINJ is the OpenACC internal library for profiling.
 .
 This package contains the 64-bit ACCINJ runtime library.

Package: libcaffe-cuda-dev
Description-md5: 045f57a94680a15e78b7c646916fba70
Description-en: development files for Caffe (CUDA)
 Caffe is a deep learning framework made with expression, speed,
 and modularity in mind. It is developed by the Berkeley AI Research
 Lab (BAIR) and community contributors.
 .
 It contains the development files of caffe.

Package: libcaffe-cuda1
Description-md5: 7aaab677789490769df141ec809753b7
Description-en: library of Caffe, deep leanring framework (CUDA)
 Caffe is a deep learning framework made with expression, speed,
 and modularity in mind. It is developed by the Berkeley AI Research
 Lab (BAIR)and community contributors.
 .
 It contains caffe shared library, configured with CUDA.

Package: libcublas10.0
Description-md5: 5d0c77d8f2c8429e53892a3a70d407c4
Description-en: NVIDIA cuBLAS Library
 The Compute Unified Device Architecture (CUDA) enables NVIDIA
 graphics processing units (GPUs) to be used for massively parallel
 general purpose computation.
 .
 The cuBLAS library is an implementation of BLAS (Basic Linear Algebra
 Subprograms) on top of the NVIDIA CUDA runtime. It allows the user to access
 the computational resources of NVIDIA Graphics Processing Unit (GPU), but
 does not auto-parallelize across multiple GPUs.
 .
 This package contains the cuBLAS runtime library.

Package: libcudart10.0
Description-md5: d81acb8bf87762012a0607e71f8eff2e
Description-en: NVIDIA CUDA Runtime Library
 The Compute Unified Device Architecture (CUDA) enables NVIDIA
 graphics processing units (GPUs) to be used for massively parallel
 general purpose computation.
 .
 This package contains the CUDA Runtime API library for high-level CUDA
 programming, on top of the CUDA Driver API.

Package: libcufft10.0
Description-md5: 323d8bcdb5ce372c028cb925743b7ad1
Description-en: NVIDIA cuFFT Library
 The Compute Unified Device Architecture (CUDA) enables NVIDIA
 graphics processing units (GPUs) to be used for massively parallel
 general purpose computation.
 .
 The FFT is a divide-and-conquer algorithm for efficiently computing discrete
 Fourier transforms of complex or real-valued data sets. It is one of the most
 important and widely used numerical algorithms in computational physics and
 general signal processing. The cuFFT library provides a simple interface for
 computing FFTs on an NVIDIA GPU, which allows users to quickly leverage the
 floating-point power and parallelism of the GPU in a highly optimized and
 tested FFT library.
 .
 This package contains the cuFFT runtime library.

Package: libcufftw10.0
Description-md5: 12b7b2ed306369c1f0fb326c4e9feefa
Description-en: NVIDIA cuFFTW Library
 The Compute Unified Device Architecture (CUDA) enables NVIDIA
 graphics processing units (GPUs) to be used for massively parallel
 general purpose computation.
 .
 The FFT is a divide-and-conquer algorithm for efficiently computing discrete
 Fourier transforms of complex or real-valued data sets. It is one of the most
 important and widely used numerical algorithms in computational physics and
 general signal processing. The cuFFT library provides a simple interface for
 computing FFTs on an NVIDIA GPU, which allows users to quickly leverage the
 floating-point power and parallelism of the GPU in a highly optimized and
 tested FFT library.
 .
 This package contains the cuFFTW runtime library.

Package: libcuinj64-10.0
Description-md5: 9eca092b41526faa574873e622a445e3
Description-en: NVIDIA CUINJ Library (64-bit)
 The Compute Unified Device Architecture (CUDA) enables NVIDIA
 graphics processing units (GPUs) to be used for massively parallel
 general purpose computation.
 .
 CUINJ is the CUDA internal library for profiling.
 .
 This package contains the 64-bit CUINJ runtime library.

Package: libcupti-dev
Description-md5: 49cdc8386d120bdf14c58bebe2b3388d
Description-en: NVIDIA CUDA Profiler Tools Interface development files
 The CUDA Profiler Tools Interface (CUPTI) enables the creation of
 profiling and tracing tools that target CUDA applications.  CUPTI
 provides a set of APIs targeted at ISVs creating profilers and other
 performance optimization tools. The CUPTI APIs are not intended to be
 used by developers in their CUDA applications.
 .
 This package contains the development files: headers and libraries.

Package: libcupti-doc
Description-md5: 0c6d9f272f89c82663423610515dd3eb
Description-en: NVIDIA CUDA Profiler Tools Interface documentation
 The CUDA Profiler Tools Interface (CUPTI) enables the creation of
 profiling and tracing tools that target CUDA applications.  CUPTI
 provides a set of APIs targeted at ISVs creating profilers and other
 performance optimization tools. The CUPTI APIs are not intended to be
 used by developers in their CUDA applications.
 .
 This package contains the documentation and examples.

Package: libcupti10.0
Description-md5: 65f2f1bea81316b239224ffd680c5064
Description-en: NVIDIA CUDA Profiler Tools Interface runtime library
 The CUDA Profiler Tools Interface (CUPTI) enables the creation of
 profiling and tracing tools that target CUDA applications.  CUPTI
 provides a set of APIs targeted at ISVs creating profilers and other
 performance optimization tools. The CUPTI APIs are not intended to be
 used by developers in their CUDA applications.
 .
 This package contains the runtime library.

Package: libcurand10.0
Description-md5: 05f7818fdfa9c23c51a9db2407910cae
Description-en: NVIDIA cuRAND Library
 The Compute Unified Device Architecture (CUDA) enables NVIDIA
 graphics processing units (GPUs) to be used for massively parallel
 general purpose computation.
 .
 The cuRAND library provides facilities that focus on the simple and efficient
 generation of high-quality pseudorandom and quasirandom numbers.
 A pseudorandom sequence of numbers satisfies most of the statistical
 properties of a truly random sequence but is generated by a deterministic
 algorithm. A quasirandom sequence of n-dimensional points is generated by a
 deterministic algorithm designed to fill an n-dimensional space evenly.
 .
 This package contains the cuRAND runtime library.

Package: libcusolver10.0
Description-md5: a30cfc560fa3fdce643526b0177ea7ba
Description-en: NVIDIA cuSOLVER Library
 The cuSOLVER library contains LAPACK-like functions in dense and sparse
 linear algebra, including linear solver, least-square solver and eigenvalue
 solver.
 .
 This package contains the cuSOLVER runtime library.

Package: libcusparse10.0
Description-md5: 12835bf1c971845122d660784efe714a
Description-en: NVIDIA cuSPARSE Library
 The Compute Unified Device Architecture (CUDA) enables NVIDIA
 graphics processing units (GPUs) to be used for massively parallel
 general purpose computation.
 .
 The cuSPARSE library contains a set of basic linear algebra subroutines used
 for handling sparse matrices. It is implemented on top of the NVIDIA CUDA
 runtime and is designed to be called from C and C++. The library routines can
 be classified into four categories:
  * Level 1: operations between a vector in sparse format and a vector in dense
 format
  * Level 2: operations between a matrix in sparse format and a vector in dense
 format
  * Level 3: operations between a matrix in sparse format and a set of vectors
 in dense format
  * Conversion: operations that allow conversion between different matrix
 formats
 .
 This package contains the cuSPARSE runtime library.

Package: libegl-nvidia-legacy-390xx0
Description-md5: a60fa61c318ab2a59c76e662ff249e83
Description-en: NVIDIA binary EGL library (390xx legacy version)
 EGL provides a platform-agnostic mechanism for creating rendering surfaces
 for use with other graphics libraries, such as OpenGL|ES.
 .
 See the description of the nvidia-legacy-390xx-driver package
 or /usr/share/doc/libgl1-nvidia-legacy-390xx-glx/README.txt.gz
 for a complete list of supported GPUs and PCI IDs.
 .
 This package contains the driver specific binary EGL implementation provided
 by NVIDIA that is accessed via GLVND.

Package: libegl1-nvidia-legacy-390xx
Description-md5: 56e4f886c9f3c72eb4dc99d9cbd8b367
Description-en: NVIDIA binary EGL library (non-GLVND variant) (390xx legacy version)
 EGL provides a platform-agnostic mechanism for creating rendering surfaces
 for use with other graphics libraries, such as OpenGL|ES.
 .
 This package contains the driver specific binary EGL implementation by NVIDIA
 as a non-GLVND alternative.

Package: libeztrace0-contrib
Description-md5: ba2d662be1addce52f1f9349d0d09d9a
Description-en: Automatic execution trace generation for HPC - development files
 EZTrace is a tool that aims at generating automatically execution traces
 from HPC (High Performance Computing) programs. It generates execution
 trace files that can be interpreted by visualization tools such as
 ViTE. It uses LD_PRELOAD and dlsym() to intercept calls to the usual HPC
 primitives, to be observed.
 .
 This package contains the development files depending on CUDA.

Package: libgl1-nvidia-legacy-390xx-glvnd-glx
Description-md5: 049da4b12db475f89190cb58028d8632
Description-en: NVIDIA binary OpenGL/GLX library (GLVND variant) (390xx legacy version)
 The NVIDIA binary driver provides optimized hardware acceleration of
 OpenGL/GLX/EGL/GLES applications via a direct-rendering X Server
 for graphics cards using NVIDIA chip sets.
 .
 See the description of the nvidia-legacy-390xx-driver package
 or /usr/share/doc/libgl1-nvidia-legacy-390xx-glvnd-glx/README.txt.gz
 for a complete list of supported GPUs and PCI IDs.
 .
 This metapackage depends on the NVIDIA binary OpenGL/GLX implementation using
 GLVND and the corresponding GLVND loader library.

Package: libgl1-nvidia-legacy-390xx-glx
Description-md5: 612f17e0be17de4159cf2ad1070501ca
Description-en: NVIDIA binary OpenGL/GLX library (non-GLVND variant) (390xx legacy version)
 The NVIDIA binary driver provides optimized hardware acceleration of
 OpenGL/GLX/EGL/GLES applications via a direct-rendering X Server
 for graphics cards using NVIDIA chip sets.
 .
 See the description of the nvidia-legacy-390xx-driver package
 or /usr/share/doc/libgl1-nvidia-legacy-390xx-glx/README.txt.gz
 for a complete list of supported GPUs and PCI IDs.
 .
 This package contains the driver specific binary OpenGL/GLX implementation
 provided by NVIDIA as a non-GLVND alternative.

Package: libgles-nvidia-legacy-390xx1
Description-md5: bea4ea39959ebbdd25de98f49d216cb7
Description-en: NVIDIA binary OpenGL|ES 1.x library (390xx legacy version)
 OpenGL|ES is a cross-platform API for full-function 2D and 3D graphics on
 embedded systems - including consoles, phones, appliances and vehicles.
 It contains a subset of OpenGL plus a number of extensions for the
 special needs of embedded systems.
 .
 OpenGL|ES 1.x provides an API for fixed-function hardware.
 .
 See the description of the nvidia-legacy-390xx-driver package
 or /usr/share/doc/libgl1-nvidia-legacy-390xx-glx/README.txt.gz
 for a complete list of supported GPUs and PCI IDs.
 .
 This package contains the driver specific binary OpenGL|ES 1.x implementation
 by NVIDIA that is accessed via GLVND.

Package: libgles-nvidia-legacy-390xx2
Description-md5: 52f9840920a47f7e524faed24b4b3b9b
Description-en: NVIDIA binary OpenGL|ES 2.x library (390xx legacy version)
 OpenGL|ES is a cross-platform API for full-function 2D and 3D graphics on
 embedded systems - including consoles, phones, appliances and vehicles.
 It contains a subset of OpenGL plus a number of extensions for the
 special needs of embedded systems.
 .
 OpenGL|ES 2.x provides an API for programmable hardware including vertex
 and fragment shaders.
 .
 See the description of the nvidia-legacy-390xx-driver package
 or /usr/share/doc/libgl1-nvidia-legacy-390xx-glx/README.txt.gz
 for a complete list of supported GPUs and PCI IDs.
 .
 This package contains the driver specific binary OpenGL|ES 2.x implementation
 by NVIDIA that is accessed via GLVND.

Package: libglx-nvidia-legacy-390xx0
Description-md5: ec43a951270bda02c306b14de9e8e007
Description-en: NVIDIA binary GLX library (390xx legacy version)
 GLX ("OpenGL Extension to the X Window System") provides an interface between
 OpenGL and the X Window System as well as extensions to OpenGL itself.
 .
 See the description of the nvidia-legacy-390xx-driver package
 or /usr/share/doc/libgl1-nvidia-legacy-390xx-glx/README.txt.gz
 for a complete list of supported GPUs and PCI IDs.
 .
 This package contains the driver specific binary GLX implementation by NVIDIA
 that is accessed via GLVND.

Package: libhwloc-contrib-plugins
Description-md5: 2d290bacc6b14b74eb41e176e94c8d01
Description-en: Hierarchical view of the machine - contrib plugins
 libhwloc provides a portable abstraction (across OS, versions,
 architectures, ...) of the hierarchical topology of modern architectures. It
 primarily aims at helping high-performance computing applications with
 gathering information about the hardware so as to exploit it accordingly and
 efficiently.
 .
 libhwloc provides a hierarchical view of the machine, NUMA memory nodes,
 sockets, shared caches, cores and simultaneous multithreading. It also gathers
 various attributes such as cache and memory information.
 .
 libhwloc supports old kernels not having sysfs topology information,
 with knowledge of cpusets, offline cpus, and Kerrighed support
 .
 This package contains plugins to add discovery support for non-free items. This
 includes
  - CUDA support.
  - nvctrl support.

Package: libnppc10.0
Description-md5: 371330c0e4596d14c1ae0b854eecfa9c
Description-en: NVIDIA Performance Primitives core runtime library
 NVIDIA NPP is a library of functions for performing CUDA accelerated
 processing.  The initial set offunctionality in the library focuses on imaging
 and video processing and is widely applicable for developers in these areas.
 NPP will evolve over time to encompass more of the compute heavy tasks in a
 variety of problem domains.  The NPP library is written to maximize
 flexibility, while maintaining high performance.
 .
 This package contains the NVIDIA Performance Primitives core runtime library.

Package: libnppial10.0
Description-md5: bedf7547f548db0fc00407b8bb5572af
Description-en: NVIDIA Performance Primitives lib for Image Arithmetic and Logic
 NVIDIA NPP is a library of functions for performing CUDA accelerated
 processing.
 .
 This package contains the NVIDIA Performance Primitives runtime library for
 Image Arithmetic and Logic operations, which is a sub-library of nppi.

Package: libnppicc10.0
Description-md5: 5c43692beedf3cbda68cb3add3a2f55f
Description-en: NVIDIA Performance Primitives lib for Image Color Conversion
 NVIDIA NPP is a library of functions for performing CUDA accelerated
 processing.
 .
 This package contains the NVIDIA Performance Primitives runtime library for
 Image Color and sampling Conversion, which is a sub-library of nppi.

Package: libnppicom10.0
Description-md5: f57c760189c436ceb9bcb374fc263a14
Description-en: NVIDIA Performance Primitives lib for Image Compression
 NVIDIA NPP is a library of functions for performing CUDA accelerated
 processing.
 .
 This package contains the NVIDIA Performance Primitives runtime library for
 Image Compression, which is a sub-library of nppi.

Package: libnppidei10.0
Description-md5: 96ae21f243272a0c4cf855590b806f52
Description-en: NVIDIA Performance Primitives lib for Image Data Exchange and Initialization
 NVIDIA NPP is a library of functions for performing CUDA accelerated
 processing.
 .
 This package contains the NVIDIA Performance Primitives runtime library for
 Image Data Exchange and Initialization, which is a sub-library of nppi.

Package: libnppif10.0
Description-md5: 25b0779ba1f9f488a81add47bc943c6a
Description-en: NVIDIA Performance Primitives lib for Image Filters
 NVIDIA NPP is a library of functions for performing CUDA accelerated
 processing.
 .
 This package contains the NVIDIA Performance Primitives runtime library for
 Image Filters, which is a sub-library of nppi.

Package: libnppig10.0
Description-md5: 1f9ccb01fdd747cf6305e037d1c19323
Description-en: NVIDIA Performance Primitives lib for Image Geometry transforms
 NVIDIA NPP is a library of functions for performing CUDA accelerated
 processing.
 .
 This package contains the NVIDIA Performance Primitives runtime library for
 Image Geometry transforms, which is a sub-library of nppi.

Package: libnppim10.0
Description-md5: f03f24db87826ca6b27a88b05bc823c0
Description-en: NVIDIA Performance Primitives lib for Image Morphological operations
 NVIDIA NPP is a library of functions for performing CUDA accelerated
 processing.
 .
 This package contains the NVIDIA Performance Primitives runtime library for
 Image Morphological operations, which is a sub-library of nppi.

Package: libnppist10.0
Description-md5: c9507fd21beb3b0609afeb40e63f4792
Description-en: NVIDIA Performance Primitives lib for Image Statistics
 NVIDIA NPP is a library of functions for performing CUDA accelerated
 processing.
 .
 This package contains the NVIDIA Performance Primitives runtime library for
 Image Statistics and Linear Transformation, which is a sub-library of nppi.

Package: libnppisu10.0
Description-md5: 37b8fa6ff382e1378e1a21c6d0074f80
Description-en: NVIDIA Performance Primitives lib for Image Support
 NVIDIA NPP is a library of functions for performing CUDA accelerated
 processing.
 .
 This package contains the NVIDIA Performance Primitives runtime library for
 Image Support, which is a sub-library of nppi.

Package: libnppitc10.0
Description-md5: d1f0489937a1662cf9d2bf3e68764e1f
Description-en: NVIDIA Performance Primitives lib for Image Threshold and Compare
 NVIDIA NPP is a library of functions for performing CUDA accelerated
 processing.
 .
 This package contains the NVIDIA Performance Primitives runtime library for
 Image Threshold and Compare, which is a sub-library of nppi.

Package: libnpps10.0
Description-md5: 24aac8a47c80e58916c189d81d7a7714
Description-en: NVIDIA Performance Primitives for signal processing runtime library
 NVIDIA NPP is a library of functions for performing CUDA accelerated
 processing.  The initial set offunctionality in the library focuses on imaging
 and video processing and is widely applicable for developers in these areas.
 NPP will evolve over time to encompass more of the compute heavy tasks in a
 variety of problem domains.  The NPP library is written to maximize
 flexibility, while maintaining high performance.
 .
 This package contains the NVIDIA Performance Primitives runtime library for
 signal processing.

Package: libnvblas10.0
Description-md5: e77f7a1f1173b44f2910c8a51eff1d1c
Description-en: NVBLAS runtime library
 The Compute Unified Device Architecture (CUDA) enables NVIDIA
 graphics processing units (GPUs) to be used for massively parallel
 general purpose computation.
 .
 The NVBLAS Library is a GPU-accelerated Library that implements BLAS (Basic
 Linear Algebra Subprograms). It can accelerate most BLAS Level-3 routines by
 dynamically routing BLAS calls to one or more NVIDIA GPUs present in the
 system, when the characteristics of the call make it to speedup on a GPU.

Package: libnvgraph10.0
Description-md5: 988a9919199874ca27432f48a0aa4472
Description-en: NVIDIA Graph Analytics library (nvGRAPH)
 The NVIDIA Graph Analytics library (nvGRAPH) comprises of parallel
 algorithms for high performance analytics on graphs with up to 2 billion
 edges. nvGRAPH makes it possible to build interactive and high throughput
 graph analytics applications.
 .
 nvGRAPH supports three widely-used algorithms:
  * [Page Rank] is most famously used in search engines, and also used in
    social network analysis, recommendation systems, and for novel uses
    in natural science when studying the relationship between proteins
    and in ecological networks.
  * [Single Source Shortest Path] is used to identify the fastest path from
    A to B through a road network, and can also be used for a optimizing
    a wide range of other logistics problems.
  * [Single Source Widest Path] is used in domains like IP traffic routing
    and traffic-sensitive path planning.
 .
 In addition, the nvGRAPH semi-ring SPMV operations can be used to build
 a wide range of innovative graph traversal algorithms.
 .
 nvGRAPH accelerates analysis of large graphs by making efficient use of
 the massive parallelism available in NVIDIA Tesla GPUs. The size of a
 graph in memory is dominated by the number of edges.
 An M40 with 24 GB can support a graph of up to 2 billion edges.

Package: libnvidia-legacy-390xx-cfg1
Description-md5: 9bc82c7ad9f1a6f632677e0edafb1e73
Description-en: NVIDIA binary OpenGL/GLX configuration library (390xx legacy version)
 The NVIDIA binary driver provides optimized hardware acceleration of
 OpenGL/GLX/EGL/GLES applications via a direct-rendering X Server
 for graphics cards using NVIDIA chip sets.
 .
 This package contains the libnvidia-cfg.so.1 runtime library.

Package: libnvidia-legacy-390xx-compiler
Description-md5: 42631eb26dc8e58eb181c53d89c5ef2c
Description-en: NVIDIA runtime compiler library (390xx legacy version)
 The Compute Unified Device Architecture (CUDA) enables NVIDIA
 graphics processing units (GPUs) to be used for massively parallel
 general purpose computation.
 .
 This package contains the runtime compiler library.

Package: libnvidia-legacy-390xx-cuda1
Description-md5: d250bd11acb307c5af49d23fd656c497
Description-en: NVIDIA CUDA Driver Library (390xx legacy version)
 The Compute Unified Device Architecture (CUDA) enables NVIDIA
 graphics processing units (GPUs) to be used for massively parallel
 general purpose computation.
 .
 This package contains the CUDA Driver API library for low-level CUDA
 programming.
 .
 Supported NVIDIA devices include GPUs starting from GeForce 8 and Quadro FX
 series, as well as the Tesla computing processors.
 .
 Please see the nvidia-legacy-390xx-kernel-dkms or
 nvidia-legacy-390xx-kernel-source packages
 for building the kernel module required by this package.
 This will provide nvidia-legacy-390xx-kernel-390.87.

Package: libnvidia-legacy-390xx-eglcore
Description-md5: 0a0fe1aac2d4033df056938908ded337
Description-en: NVIDIA binary EGL core libraries (390xx legacy version)
 EGL provides a platform-agnostic mechanism for creating rendering surfaces
 for use with other graphics libraries, such as OpenGL|ES.
 .
 OpenGL|ES is a cross-platform API for full-function 2D and 3D graphics on
 embedded systems - including consoles, phones, appliances and vehicles.
 It contains a subset of OpenGL plus a number of extensions for the
 special needs of embedded systems.
 .
 This package contains the private core libraries used by the NVIDIA
 implementation of EGL and OpenGL|ES.

Package: libnvidia-legacy-390xx-encode1
Description-md5: c87b9e738ce53112a391c56e9788dffd
Description-en: NVENC Video Encoding runtime library (390xx legacy version)
 The NVENC Video Encoding library provides an interface to video encoder
 hardware on supported NVIDIA GPUs.
 .
 This package contains the nvidia-encode runtime library.

Package: libnvidia-legacy-390xx-fatbinaryloader
Description-md5: 975b8d8bd68647a620b35307f3e8390e
Description-en: NVIDIA FAT binary loader (390xx legacy version)
 The Compute Unified Device Architecture (CUDA) enables NVIDIA
 graphics processing units (GPUs) to be used for massively parallel
 general purpose computation.
 .
 This package contains the FAT multiarchitecture binary loader.

Package: libnvidia-legacy-390xx-fbc1
Description-md5: bde377fb65e2587d6855072c3533632c
Description-en: NVIDIA OpenGL-based Framebuffer Capture runtime library (390xx legacy version)
 The NVIDIA OpenGL-based Framebuffer Capture (NvFBCOpenGL) library provides
 a high performance, low latency interface to capture and optionally encode an
 OpenGL framebuffer. NvFBCOpenGL is a private API that is only available to
 approved partners for use in remote graphics scenarios.
 .
 This package contains the NvFBCOpenGL runtime library.

Package: libnvidia-legacy-390xx-glcore
Description-md5: b18f86a6f16227d3e4a96572591066ff
Description-en: NVIDIA binary OpenGL/GLX core libraries (390xx legacy version)
 The NVIDIA binary driver provides optimized hardware acceleration of
 OpenGL/GLX/EGL/GLES applications via a direct-rendering X Server
 for graphics cards using NVIDIA chip sets.
 .
 This package contains the private core libraries used by the NVIDIA
 implementation of OpenGL and GLX.

Package: libnvidia-legacy-390xx-ifr1
Description-md5: 3ae915d7da7ca6369aa180b408066b35
Description-en: NVIDIA OpenGL-based Inband Frame Readback runtime library (390xx legacy)
 The NVIDIA OpenGL-based Inband Frame Readback (NvIFROpenGL) library provides
 a high performance, low latency interface to capture and optionally encode an
 OpenGL framebuffer. NvIFROpenGL is a private API that is only available to
 approved partners for use in remote graphics scenarios.
 .
 This package contains the NvIFROpenGL runtime library.

Package: libnvidia-legacy-390xx-ml1
Description-md5: 11bde97b7c0a056cc8a84831f6b310be
Description-en: NVIDIA Management Library (NVML) runtime library (390xx legacy version)
 The NVIDIA Management Library (NVML) provides a monitoring and management API.
 It provides a direct access to the queries and commands exposed via nvidia-smi.
 .
 This package contains the nvidia-ml runtime library.

Package: libnvidia-legacy-390xx-nvcuvid1
Description-md5: 8e3dc8f79228a41fd669f0a94c9da901
Description-en: NVIDIA CUDA Video Decoder runtime library (390xx legacy version)
 The Compute Unified Device Architecture (CUDA) enables NVIDIA
 graphics processing units (GPUs) to be used for massively parallel
 general purpose computation.
 .
 The NVIDIA CUDA Video Decoder (NVCUVID) library provides an interface to
 hardware video decoding capabilities on NVIDIA GPUs with CUDA.
 .
 This package contains the nvcuvid runtime library.

Package: libnvidia-legacy-390xx-ptxjitcompiler1
Description-md5: ed5e6ab18dbeab781c9e355bfa51d483
Description-en: NVIDIA PTX JIT Compiler (390xx legacy version)
 The Compute Unified Device Architecture (CUDA) enables NVIDIA
 graphics processing units (GPUs) to be used for massively parallel
 general purpose computation.
 .
 This package contains the runtime PTX JIT compiler library.

Package: libnvjpeg10.0
Description-md5: 623ac7d33181f0192b9126244e553a5c
Description-en: NVIDIA JPEG library (nvJPEG)
 The nvJPEG 1.0 library provides high-performance, GPU accelerated JPEG
 decoding functionality for image formats commonly used in deep learning and
 hyperscale multimedia applications. The library offers single and batched JPEG
 decoding capabilities which efficiently utilize the available GPU resources
 for optimum performance; and the flexibility for users to manage the memory
 allocation needed for decoding.

Package: libnvrtc10.0
Description-md5: b1b9b1f5f271a283664f94ae0f1e94b4
Description-en: CUDA Runtime Compilation (NVIDIA NVRTC Library)
 CUDA Runtime Compilation library (nvrtc) provides an API to compile
 CUDA-C++ device source code at runtime.
 .
 The resulting compiled PTX can be launched on a GPU using the CUDA
 Driver API.
 .
 This package contains the NVRTC library.

Package: libnvtoolsext1
Description-md5: 301861470547d1207ee0ad56dfa1ef90
Description-en: NVIDIA Tools Extension Library
 The NVIDIA Tools Extension SDK (NVTX) is a C-based API for marking events and
 ranges in your applications. Applications which integrate NVTX can use Nsight
 to capture and visualize these events and ranges.
 .
 This package contains the NVIDIA Tools Extension runtime library.

Package: libnvvm3
Description-md5: 1efd5ade308f30b7de84c0430187211c
Description-en: NVIDIA NVVM Library
 NVIDIA's CUDA Compiler (NVCC) is based on the widely used LLVM open source
 compiler infrastructure.
 .
 The NVVM library is used by NVCC to compile CUDA binary code to run on NVIDIA
 GPUs.
 .
 This package contains the NVIDIA NVVM runtime library.

Package: libsocl-contrib-1.2-0
Description-md5: 4a69ce3f02f2cf01466ed8b9a22d8758
Description-en: Task scheduler for heterogeneous multicore machines
 StarPU is a runtime system that offers support for heterogeneous
 multicore machines. While many efforts are devoted to design efficient
 computation kernels for those architectures (e.g. to implement BLAS
 kernels on GPUs or on Cell's SPUs), StarPU not only takes care of
 offloading such kernels (and implementing data coherency across
 the machine), but it also makes sure the kernels are executed as
 efficiently as possible.
 .
 This package contains an OpenCL-compatible library interface to StarPU.
 This "contrib" version is built against CUDA.

Package: libstarpu-contrib-1.2-5
Description-md5: f0ca811d02d6bbb6c830781bf9811bff
Description-en: Task scheduler for heterogeneous multicore machines
 StarPU is a runtime system that offers support for heterogeneous
 multicore machines. While many efforts are devoted to design efficient
 computation kernels for those architectures (e.g. to implement BLAS
 kernels on GPUs or on Cell's SPUs), StarPU not only takes care of
 offloading such kernels (and implementing data coherency across
 the machine), but it also makes sure the kernels are executed as
 efficiently as possible.
 .
 This package contains the main StarPU library
 This "contrib" version is built against CUDA.

Package: libstarpu-contrib-dev
Description-md5: e7f42763fbe9c087a6defa203190f56e
Description-en: Task scheduler for heterogeneous multicore machines - dev
 StarPU is a runtime system that offers support for heterogeneous
 multicore machines. While many efforts are devoted to design efficient
 computation kernels for those architectures (e.g. to implement BLAS
 kernels on GPUs or on Cell's SPUs), StarPU not only takes care of
 offloading such kernels (and implementing data coherency across
 the machine), but it also makes sure the kernels are executed as
 efficiently as possible.
 .
 This package contains development headers and libraries.
 This "contrib" version is built against CUDA.

Package: libstarpu-contribfft-1.2-0
Description-md5: 1079179920c93a735ab89566a3855d36
Description-en: Task scheduler for heterogeneous multicore machines
 StarPU is a runtime system that offers support for heterogeneous
 multicore machines. While many efforts are devoted to design efficient
 computation kernels for those architectures (e.g. to implement BLAS
 kernels on GPUs or on Cell's SPUs), StarPU not only takes care of
 offloading such kernels (and implementing data coherency across
 the machine), but it also makes sure the kernels are executed as
 efficiently as possible.
 .
 This package contains a hybrid CPU+GPU FFT library.
 This "contrib" version is built against CUDA.

Package: libstarpu-contribmpi-1.2-3
Description-md5: 4d3ce5602db3c9fbd91b7e19dcc14e2a
Description-en: Task scheduler for heterogeneous multicore machines
 StarPU is a runtime system that offers support for heterogeneous
 multicore machines. While many efforts are devoted to design efficient
 computation kernels for those architectures (e.g. to implement BLAS
 kernels on GPUs or on Cell's SPUs), StarPU not only takes care of
 offloading such kernels (and implementing data coherency across
 the machine), but it also makes sure the kernels are executed as
 efficiently as possible.
 .
 This package contains MPI extensions for StarPU.
 This "contrib" version is built against CUDA.

Package: libthrust-dev
Description-md5: 5c04cf9edc908ae75dc0cc67ca840a33
Description-en: Thrust - Parallel Algorithms Library
 Thrust is a parallel algorithms library which resembles the C++ Standard
 Template Library (STL). Thrust's high-level interface greatly enhances
 programmer productivity while enabling performance portability between GPUs
 and multicore CPUs. Interoperability with established technologies (such as
 CUDA, TBB, and OpenMP) facilitates integration with existing software.

Package: nsight-compute
Description-md5: ae73c3bbcf7dbd4b9a83f57a09c0d8b3
Description-en: NVIDIA Nsight Compute
 NVIDIA Nsight Compute is an interactive kernel profiler for CUDA applications.
 It provides detailed performance metrics and API debugging via a user
 interface and command line tool. In addition, its baseline feature allows
 users to compare results within the tool. Nsight Compute provides a
 customizable and data-driven user interface and metric collection and can be
 extended with analysis scripts for post-processing results.

Package: nvidia-cuda-dev
Description-md5: 23a17262479fe7daf1cae67727e949c7
Description-en: NVIDIA CUDA development files
 The Compute Unified Device Architecture (CUDA) enables NVIDIA
 graphics processing units (GPUs) to be used for massively parallel
 general purpose computation.
 .
 This package contains the development files: headers and libraries.

Package: nvidia-cuda-doc
Description-md5: 1ade4ee74357654b5ef0998d191b1349
Description-en: NVIDIA CUDA and OpenCL documentation
 The Compute Unified Device Architecture (CUDA) enables NVIDIA
 graphics processing units (GPUs) to be used for massively parallel
 general purpose computation.
 .
 OpenCL (Open Computing Language) is a multi-vendor open standard for
 general-purpose parallel programming of heterogeneous systems that include
 CPUs, GPUs and other processors.
 .
 This package contains the developer documentation.

Package: nvidia-cuda-gdb
Description-md5: 03d8613224997399b5d081ffb05a91f3
Description-en: NVIDIA CUDA Debugger (GDB)
 The Compute Unified Device Architecture (CUDA) enables NVIDIA
 graphics processing units (GPUs) to be used for massively parallel
 general purpose computation.
 .
 This package contains the cuda-gdb debugger.

Package: nvidia-cuda-toolkit
Description-md5: 4df65757189fdcbdcc50ffa97fccca02
Description-en: NVIDIA CUDA development toolkit
 The Compute Unified Device Architecture (CUDA) enables NVIDIA
 graphics processing units (GPUs) to be used for massively parallel
 general purpose computation.
 .
 This package contains the nvcc compiler and other tools needed for building
 CUDA applications.
 .
 Running CUDA applications requires a supported NVIDIA GPU and the NVIDIA
 driver kernel module.

Package: nvidia-legacy-390xx-alternative
Description-md5: 920ec173cdd5c9c2b323ad02ecdecc5b
Description-en: allows the selection of NVIDIA as GLX provider (390xx legacy version)
 In setups with several NVIDIA driver versions installed (e.g. current and
 legacy) this metapackage registers an alternative to allow easy switching
 between the different versions.
 .
 Use 'update-glx --config nvidia' to select a version.
 .
 This package does not depend on the corresponding NVIDIA libraries.
 In order to install the NVIDIA driver and libraries, install the
 nvidia-legacy-390xx-driver package instead.

Package: nvidia-legacy-390xx-driver
Description-md5: 5b7fbae6998e9d10ee1f176bf04f8c58
Description-en: NVIDIA metapackage (390xx legacy version)
 This metapackage depends on the NVIDIA binary driver and libraries
 that provide optimized hardware acceleration of
 OpenGL/GLX/EGL/GLES/Vulkan applications via a direct-rendering X Server.
 .
 Please see the nvidia-legacy-390xx-kernel-dkms or
 nvidia-legacy-390xx-kernel-source packages
 for building the kernel module required by this package.
 This will provide nvidia-legacy-390xx-kernel-390.87.
 .
 This legacy version is the last release that supports the following GPUs:
 GeForce 410M [GF119M], GeForce 510 [GF119], GeForce 605 [GF119],
 GeForce 610M [GF108M], GeForce 610M [GF119M], GeForce 610M [GF117M],
 GeForce 705M [GF119M], GeForce 710M [GF117M], GeForce 810M [GF117M],
 GeForce 820M [GF117M], GeForce GT 415M [GF108M], GeForce GT 420 [GF108],
 GeForce GT 420M [GF108M], GeForce GT 425M [GF108M], GeForce GT 430 [GF108],
 GeForce GT 435M [GF106M], GeForce GT 435M [GF108M], GeForce GT 440 [GF106],
 GeForce GT 440 [GF108], GeForce GT 445M [GF106M], GeForce GT 520 [GF108],
 GeForce GT 520 [GF119], GeForce GT 520M [GF108M], GeForce GT 520M [GF119M],
 GeForce GT 520MX [GF119M], GeForce GT 525M [GF108M], GeForce GT 530 [GF108],
 GeForce GT 540M [GF108M], GeForce GT 545 OEM [GF116], GeForce GT 545 [GF116],
 GeForce GT 550M [GF106M], GeForce GT 550M [GF108M], GeForce GT 550M [GF116M],
 GeForce GT 555M [GF106M], GeForce GT 555M [GF108M], GeForce GT 555M [GF116M],
 GeForce GT 560M [GF116M], GeForce GT 610 [GF108], GeForce GT 610 [GF119],
 GeForce GT 620 OEM [GF119], GeForce GT 620 [GF108], GeForce GT 620M [GF108M],
 GeForce GT 620M [GF117M], GeForce GT 620M LE [GF108M],
 GeForce GT 625 OEM [GF119], GeForce GT 625M [GF117M], GeForce GT 630 [GF108],
 GeForce GT 630M [GF117M], GeForce GT 630M LE [GF108M],
 GeForce GT 635M [GF108M], GeForce GT 635M [GF116M],
 GeForce GT 635M LE [GF108M], GeForce GT 640M LE [GF108M],
 GeForce GT 640 OEM [GF116], GeForce GT 645 OEM [GF114],
 GeForce GT 705 [GF119], GeForce GT 720M [GF117M], GeForce GT 730 [GF108],
 GeForce GTS 450 OEM [GF106], GeForce GTS 450 [GF106],
 GeForce GTS 450 Rev. 2 [GF116], GeForce GTS 450 Rev. 3 [GF116],
 GeForce GTX 460 OEM [GF104], GeForce GTX 460 [GF104],
 GeForce GTX 460 v2 [GF114], GeForce GTX 460 SE [GF104],
 GeForce GTX 460 SE v2 [GF114], GeForce GTX 460M [GF106M],
 GeForce GTX 465 [GF100], GeForce GTX 470 [GF100], GeForce GTX 470M [GF104M],
 GeForce GTX 480 [GF100], GeForce GTX 480M [GF100M],
 GeForce GTX 485M [GF104M], GeForce GTX 550 Ti [GF116],
 GeForce GTX 555 [GF114], GeForce GTX 560 OEM [GF110],
 GeForce GTX 560 [GF114], GeForce GTX 560 SE [GF114],
 GeForce GTX 560 Ti [GF114], GeForce GTX 560 Ti OEM [GF110],
 GeForce GTX 560 Ti 448 Cores [GF110], GeForce GTX 570 [GF110],
 GeForce GTX 570 Rev. 2 [GF110], GeForce GTX 570M [GF114M],
 GeForce GTX 580 [GF110], GeForce GTX 580 Rev. 2 [GF110],
 GeForce GTX 580M [GF114M], GeForce GTX 590 [GF110],
 GeForce GTX 670M [GF114M], GeForce GTX 675M [GF114M], NVS 310 [GF119],
 NVS 315 [GF119], NVS 4200M [GF119M], NVS 5200M [GF108GLM],
 NVS 5400M [GF108M], Quadro 500M [GF108GLM], Quadro 600 [GF108GL],
 Quadro 1000M [GF108GLM], Quadro 2000 [GF106GL], Quadro 2000M [GF106GLM],
 Quadro 3000M [GF104GLM], Quadro 4000 [GF100GL], Quadro 4000M [GF104GLM],
 Quadro 5000 [GF100GL], Quadro 5000M [GF100GLM], Quadro 5010M [GF100GLM],
 Quadro 6000 [GF100GL], Quadro 7000 [GF100GL], Quadro NVS 4200M [GF119M],
 Tesla C2050 [GF100GL], Tesla C2050 [GF110GL], Tesla C2070 [GF100GL],
 Tesla C2075 [GF110GL], Tesla M2070 [GF100GL], Tesla M2070-Q [GF100GL],
 Tesla M2075 [GF110GL], Tesla M2090 [GF110GL], Tesla T20 Processor [GF100GL].
 .
 There are several "more modern" GPUs supported by this package, too, but the
 updated drivers in the newer legacy packages or the current nvidia-driver
 package usually provide more features and better support.
 Look at the other legacy packages for older cards.
 .
 See /usr/share/doc/nvidia-legacy-390xx-driver/README.txt.gz
 for a complete list of supported GPUs and PCI IDs.
 .
 Building the kernel module has been tested up to Linux 4.20.

Package: nvidia-legacy-390xx-driver-bin
Description-md5: 00d4cddd256da0b3b2448703e875ed33
Description-en: NVIDIA driver support binaries (390xx legacy version)
 The NVIDIA binary driver provides optimized hardware acceleration of
 OpenGL/GLX/EGL/GLES applications via a direct-rendering X Server
 for graphics cards using NVIDIA chip sets.
 .
 This package contains supporting binaries for the driver.

Package: nvidia-legacy-390xx-driver-libs
Description-md5: bffc248c9dcc39fc9ab0772c9b880247
Description-en: NVIDIA metapackage (OpenGL/GLX/EGL/GLES libraries) (390xx legacy version)
 This metapackage depends on the NVIDIA binary libraries
 that provide optimized hardware acceleration of
 OpenGL/GLX/EGL/GLES applications via a direct-rendering X Server.

Package: nvidia-legacy-390xx-driver-libs-nonglvnd
Description-md5: 885d686f8a861f637067dfb88abfe765
Description-en: NVIDIA metapackage (non-GLVND OpenGL/GLX/EGL/GLES libraries) (390xx legacy)
 This metapackage depends on the (non-GLVND) NVIDIA binary libraries
 that provide optimized hardware acceleration of
 OpenGL/GLX/EGL/GLES applications via a direct-rendering X Server.

Package: nvidia-legacy-390xx-egl-icd
Description-md5: 48bb1c6fd6391aad335bab10bc4ab1eb
Description-en: NVIDIA EGL installable client driver (ICD)
 EGL provides a platform-agnostic mechanism for creating rendering surfaces
 for use with other graphics libraries, such as OpenGL|ES.
 .
 This metapackage provides the NVIDIA installable client driver (ICD) for
 EGL via GLVND which supports NVIDIA GPUs.

Package: nvidia-legacy-390xx-kernel-dkms
Description-md5: 1d83670a2ae359152053cf1599d75214
Description-en: NVIDIA binary kernel module DKMS source (390xx legacy version)
 This package builds the NVIDIA Xorg binary kernel module needed by
 nvidia-legacy-390xx-driver, using DKMS.
 Provided that you have the kernel header packages installed, the kernel
 module will be built for your running kernel and automatically rebuilt for
 any new kernel headers that are installed.
 .
 The NVIDIA binary driver provides optimized hardware acceleration of
 OpenGL/GLX/EGL/GLES applications via a direct-rendering X Server
 for graphics cards using NVIDIA chip sets.
 .
 This legacy version is the last release that supports the following GPUs:
 GeForce 410M [GF119M], GeForce 510 [GF119], GeForce 605 [GF119],
 GeForce 610M [GF108M], GeForce 610M [GF119M], GeForce 610M [GF117M],
 GeForce 705M [GF119M], GeForce 710M [GF117M], GeForce 810M [GF117M],
 GeForce 820M [GF117M], GeForce GT 415M [GF108M], GeForce GT 420 [GF108],
 GeForce GT 420M [GF108M], GeForce GT 425M [GF108M], GeForce GT 430 [GF108],
 GeForce GT 435M [GF106M], GeForce GT 435M [GF108M], GeForce GT 440 [GF106],
 GeForce GT 440 [GF108], GeForce GT 445M [GF106M], GeForce GT 520 [GF108],
 GeForce GT 520 [GF119], GeForce GT 520M [GF108M], GeForce GT 520M [GF119M],
 GeForce GT 520MX [GF119M], GeForce GT 525M [GF108M], GeForce GT 530 [GF108],
 GeForce GT 540M [GF108M], GeForce GT 545 OEM [GF116], GeForce GT 545 [GF116],
 GeForce GT 550M [GF106M], GeForce GT 550M [GF108M], GeForce GT 550M [GF116M],
 GeForce GT 555M [GF106M], GeForce GT 555M [GF108M], GeForce GT 555M [GF116M],
 GeForce GT 560M [GF116M], GeForce GT 610 [GF108], GeForce GT 610 [GF119],
 GeForce GT 620 OEM [GF119], GeForce GT 620 [GF108], GeForce GT 620M [GF108M],
 GeForce GT 620M [GF117M], GeForce GT 620M LE [GF108M],
 GeForce GT 625 OEM [GF119], GeForce GT 625M [GF117M], GeForce GT 630 [GF108],
 GeForce GT 630M [GF117M], GeForce GT 630M LE [GF108M],
 GeForce GT 635M [GF108M], GeForce GT 635M [GF116M],
 GeForce GT 635M LE [GF108M], GeForce GT 640M LE [GF108M],
 GeForce GT 640 OEM [GF116], GeForce GT 645 OEM [GF114],
 GeForce GT 705 [GF119], GeForce GT 720M [GF117M], GeForce GT 730 [GF108],
 GeForce GTS 450 OEM [GF106], GeForce GTS 450 [GF106],
 GeForce GTS 450 Rev. 2 [GF116], GeForce GTS 450 Rev. 3 [GF116],
 GeForce GTX 460 OEM [GF104], GeForce GTX 460 [GF104],
 GeForce GTX 460 v2 [GF114], GeForce GTX 460 SE [GF104],
 GeForce GTX 460 SE v2 [GF114], GeForce GTX 460M [GF106M],
 GeForce GTX 465 [GF100], GeForce GTX 470 [GF100], GeForce GTX 470M [GF104M],
 GeForce GTX 480 [GF100], GeForce GTX 480M [GF100M],
 GeForce GTX 485M [GF104M], GeForce GTX 550 Ti [GF116],
 GeForce GTX 555 [GF114], GeForce GTX 560 OEM [GF110],
 GeForce GTX 560 [GF114], GeForce GTX 560 SE [GF114],
 GeForce GTX 560 Ti [GF114], GeForce GTX 560 Ti OEM [GF110],
 GeForce GTX 560 Ti 448 Cores [GF110], GeForce GTX 570 [GF110],
 GeForce GTX 570 Rev. 2 [GF110], GeForce GTX 570M [GF114M],
 GeForce GTX 580 [GF110], GeForce GTX 580 Rev. 2 [GF110],
 GeForce GTX 580M [GF114M], GeForce GTX 590 [GF110],
 GeForce GTX 670M [GF114M], GeForce GTX 675M [GF114M], NVS 310 [GF119],
 NVS 315 [GF119], NVS 4200M [GF119M], NVS 5200M [GF108GLM],
 NVS 5400M [GF108M], Quadro 500M [GF108GLM], Quadro 600 [GF108GL],
 Quadro 1000M [GF108GLM], Quadro 2000 [GF106GL], Quadro 2000M [GF106GLM],
 Quadro 3000M [GF104GLM], Quadro 4000 [GF100GL], Quadro 4000M [GF104GLM],
 Quadro 5000 [GF100GL], Quadro 5000M [GF100GLM], Quadro 5010M [GF100GLM],
 Quadro 6000 [GF100GL], Quadro 7000 [GF100GL], Quadro NVS 4200M [GF119M],
 Tesla C2050 [GF100GL], Tesla C2050 [GF110GL], Tesla C2070 [GF100GL],
 Tesla C2075 [GF110GL], Tesla M2070 [GF100GL], Tesla M2070-Q [GF100GL],
 Tesla M2075 [GF110GL], Tesla M2090 [GF110GL], Tesla T20 Processor [GF100GL].
 .
 There are several "more modern" GPUs supported by this package, too, but the
 updated drivers in the newer legacy packages or the current nvidia-driver
 package usually provide more features and better support.
 Look at the other legacy packages for older cards.
 .
 See /usr/share/doc/nvidia-legacy-390xx-kernel-dkms/README.txt.gz
 for a complete list of supported GPUs and PCI IDs.
 .
 This package contains the blobs for building kernel modules for the
 amd64 architecture.
 Building the kernel modules has been tested up to Linux 4.20.

Package: nvidia-legacy-390xx-kernel-source
Description-md5: 9f07971b7b4dad8d6c9037f1ea237f93
Description-en: NVIDIA binary kernel module source (390xx legacy version)
 This package provides the source for the NVIDIA Xorg binary kernel module
 needed by nvidia-legacy-390xx-driver in a form suitable
 for use by module-assistant or kernel-package.
 .
 The NVIDIA binary driver provides optimized hardware acceleration of
 OpenGL/GLX/EGL/GLES applications via a direct-rendering X Server
 for graphics cards using NVIDIA chip sets.
 .
 PLEASE read /usr/share/doc/nvidia-legacy-390xx-kernel-source/README.Debian.gz
 for building information.  If you want the kernel module to be automatically
 installed via DKMS, install nvidia-legacy-390xx-kernel-dkms instead.
 .
 This legacy version is the last release that supports the following GPUs:
 GeForce 410M [GF119M], GeForce 510 [GF119], GeForce 605 [GF119],
 GeForce 610M [GF108M], GeForce 610M [GF119M], GeForce 610M [GF117M],
 GeForce 705M [GF119M], GeForce 710M [GF117M], GeForce 810M [GF117M],
 GeForce 820M [GF117M], GeForce GT 415M [GF108M], GeForce GT 420 [GF108],
 GeForce GT 420M [GF108M], GeForce GT 425M [GF108M], GeForce GT 430 [GF108],
 GeForce GT 435M [GF106M], GeForce GT 435M [GF108M], GeForce GT 440 [GF106],
 GeForce GT 440 [GF108], GeForce GT 445M [GF106M], GeForce GT 520 [GF108],
 GeForce GT 520 [GF119], GeForce GT 520M [GF108M], GeForce GT 520M [GF119M],
 GeForce GT 520MX [GF119M], GeForce GT 525M [GF108M], GeForce GT 530 [GF108],
 GeForce GT 540M [GF108M], GeForce GT 545 OEM [GF116], GeForce GT 545 [GF116],
 GeForce GT 550M [GF106M], GeForce GT 550M [GF108M], GeForce GT 550M [GF116M],
 GeForce GT 555M [GF106M], GeForce GT 555M [GF108M], GeForce GT 555M [GF116M],
 GeForce GT 560M [GF116M], GeForce GT 610 [GF108], GeForce GT 610 [GF119],
 GeForce GT 620 OEM [GF119], GeForce GT 620 [GF108], GeForce GT 620M [GF108M],
 GeForce GT 620M [GF117M], GeForce GT 620M LE [GF108M],
 GeForce GT 625 OEM [GF119], GeForce GT 625M [GF117M], GeForce GT 630 [GF108],
 GeForce GT 630M [GF117M], GeForce GT 630M LE [GF108M],
 GeForce GT 635M [GF108M], GeForce GT 635M [GF116M],
 GeForce GT 635M LE [GF108M], GeForce GT 640M LE [GF108M],
 GeForce GT 640 OEM [GF116], GeForce GT 645 OEM [GF114],
 GeForce GT 705 [GF119], GeForce GT 720M [GF117M], GeForce GT 730 [GF108],
 GeForce GTS 450 OEM [GF106], GeForce GTS 450 [GF106],
 GeForce GTS 450 Rev. 2 [GF116], GeForce GTS 450 Rev. 3 [GF116],
 GeForce GTX 460 OEM [GF104], GeForce GTX 460 [GF104],
 GeForce GTX 460 v2 [GF114], GeForce GTX 460 SE [GF104],
 GeForce GTX 460 SE v2 [GF114], GeForce GTX 460M [GF106M],
 GeForce GTX 465 [GF100], GeForce GTX 470 [GF100], GeForce GTX 470M [GF104M],
 GeForce GTX 480 [GF100], GeForce GTX 480M [GF100M],
 GeForce GTX 485M [GF104M], GeForce GTX 550 Ti [GF116],
 GeForce GTX 555 [GF114], GeForce GTX 560 OEM [GF110],
 GeForce GTX 560 [GF114], GeForce GTX 560 SE [GF114],
 GeForce GTX 560 Ti [GF114], GeForce GTX 560 Ti OEM [GF110],
 GeForce GTX 560 Ti 448 Cores [GF110], GeForce GTX 570 [GF110],
 GeForce GTX 570 Rev. 2 [GF110], GeForce GTX 570M [GF114M],
 GeForce GTX 580 [GF110], GeForce GTX 580 Rev. 2 [GF110],
 GeForce GTX 580M [GF114M], GeForce GTX 590 [GF110],
 GeForce GTX 670M [GF114M], GeForce GTX 675M [GF114M], NVS 310 [GF119],
 NVS 315 [GF119], NVS 4200M [GF119M], NVS 5200M [GF108GLM],
 NVS 5400M [GF108M], Quadro 500M [GF108GLM], Quadro 600 [GF108GL],
 Quadro 1000M [GF108GLM], Quadro 2000 [GF106GL], Quadro 2000M [GF106GLM],
 Quadro 3000M [GF104GLM], Quadro 4000 [GF100GL], Quadro 4000M [GF104GLM],
 Quadro 5000 [GF100GL], Quadro 5000M [GF100GLM], Quadro 5010M [GF100GLM],
 Quadro 6000 [GF100GL], Quadro 7000 [GF100GL], Quadro NVS 4200M [GF119M],
 Tesla C2050 [GF100GL], Tesla C2050 [GF110GL], Tesla C2070 [GF100GL],
 Tesla C2075 [GF110GL], Tesla M2070 [GF100GL], Tesla M2070-Q [GF100GL],
 Tesla M2075 [GF110GL], Tesla M2090 [GF110GL], Tesla T20 Processor [GF100GL].
 .
 There are several "more modern" GPUs supported by this package, too, but the
 updated drivers in the newer legacy packages or the current nvidia-driver
 package usually provide more features and better support.
 Look at the other legacy packages for older cards.
 .
 See /usr/share/doc/nvidia-legacy-390xx-kernel-source/README.txt.gz
 for a complete list of supported GPUs and PCI IDs.
 .
 This package contains the blobs for building kernel modules for the
 amd64 architecture.
 Building the kernel modules has been tested up to Linux 4.20.

Package: nvidia-legacy-390xx-kernel-support
Description-md5: 16dec0bb04dd8db67c4ceb2f5fa0323d
Description-en: NVIDIA binary kernel module support files (390xx legacy version)
 The NVIDIA binary driver provides optimized hardware acceleration of
 OpenGL/GLX/EGL/GLES applications via a direct-rendering X Server
 for graphics cards using NVIDIA chip sets.
 .
 This package provides supporting configuration for the kernel module.

Package: nvidia-legacy-390xx-nonglvnd-vulkan-icd
Description-md5: 4c24014bf0d92b2a47fae6c6a02a7854
Description-en: NVIDIA Vulkan ICD (non-GLVND variant) (390xx legacy version)
 Vulkan is a multivendor open standard by the Khronos Group for 3D graphics.
 .
 This metapackage provides the NVIDIA installable client driver (ICD) for
 Vulkan (non-GLVND variant) which supports NVIDIA GPUs.

Package: nvidia-legacy-390xx-opencl-icd
Description-md5: 9abda5c94b1112eef98dad8e7adbfa70
Description-en: NVIDIA OpenCL installable client driver (ICD) (390xx legacy version)
 OpenCL (Open Computing Language) is a multivendor open standard for
 general-purpose parallel programming of heterogeneous systems that include
 CPUs, GPUs and other processors.
 .
 This package provides the NVIDIA installable client driver (ICD) for OpenCL
 which supports NVIDIA GPUs. This ICD supports OpenCL 1.x only.

Package: nvidia-legacy-390xx-smi
Description-md5: ae6beec94ae6875c481fb0e880c871d5
Description-en: NVIDIA System Management Interface (390xx legacy version)
 The NVIDIA Management Library (NVML) provides a monitoring and management API.
 The application "nvidia-smi" is the NVIDIA System Management Interface (NVSMI)
 and provides a command line interface to this functionality.
 .
 See the output from the --help command line option for supported models and
 further information.

Package: nvidia-legacy-390xx-vdpau-driver
Description-md5: 64b176180392e5ff43bdf25a5b657bba
Description-en: Video Decode and Presentation API for Unix - NVIDIA driver (390xx legacy)
 These libraries provide the Video Decode and Presentation API for Unix.
 They provide accelerated video playback (incl. H.264) for the supported
 graphics cards.
 .
 This package contains the NVIDIA VDPAU driver.
 .
 See /usr/share/doc/nvidia-legacy-390xx-vdpau-driver/README.txt.gz
 for more information.
 .
 Please see the nvidia-legacy-390xx-kernel-dkms or
 nvidia-legacy-390xx-kernel-source packages
 for building the kernel module required by this package.
 This will provide nvidia-legacy-390xx-kernel-390.87.

Package: nvidia-legacy-390xx-vulkan-icd
Description-md5: 9cec1a3a6024722fe8adc81ab9b7906d
Description-en: NVIDIA Vulkan installable client driver (ICD) (390xx legacy version)
 Vulkan is a multivendor open standard by the Khronos Group for 3D graphics.
 .
 This metapackage provides the NVIDIA installable client driver (ICD) for
 Vulkan (GLVND variant) which supports NVIDIA GPUs.

Package: nvidia-nsight
Description-md5: 24446f0932ce59989387b2d2e4309eaa
Description-en: NVIDIA Nsight Eclipse Edition
 NVIDIA Nsight Eclipse Edition is a full-featured IDE powered by the Eclipse
 platform that provides an all-in-one integrated environment to edit, build,
 debug and profile CUDA-C applications.
 Nsight Eclipse Edition supports a rich set of commercial and free plugins.

Package: nvidia-opencl-dev
Description-md5: 5404c4fac54bb1c7a833b77f92a02e84
Description-en: NVIDIA OpenCL development files
 OpenCL (Open Computing Language) is a multi-vendor open standard for
 general-purpose parallel programming of heterogeneous systems that include
 CPUs, GPUs and other processors.
 .
 This metapackage provides the development files: headers and libraries.

Package: nvidia-openjdk-8-jre
Description-md5: 214be7abd20b25b52b986addccbad9c7
Description-en: NVIDIA provided OpenJDK Java runtime, using Hotspot JIT
 Full Java runtime environment - needed for executing Java GUI and Webstart
 programs, using Hotspot JIT.
 .
 This package provides the openjdk-8 binaries shipped with the
 NVIDIA CUDA Toolkit, this obsolete version is needed for
 nvidia-visual-profiler and nvidia-nsight.

Package: nvidia-profiler
Description-md5: 83d361c54427ed94d5493552d5ade11b
Description-en: NVIDIA Profiler for CUDA and OpenCL
 The Compute Unified Device Architecture (CUDA) enables NVIDIA
 graphics processing units (GPUs) to be used for massively parallel
 general purpose computation.
 .
 OpenCL (Open Computing Language) is a multi-vendor open standard for
 general-purpose parallel programming of heterogeneous systems that include
 CPUs, GPUs and other processors.
 .
 This package contains the nvprof profiler.

Package: nvidia-settings-legacy-390xx
Description-md5: f75c396d9a17806d0f24ca39c18a71e4
Description-en: tool for configuring the NVIDIA graphics driver (390xx legacy version)
 The nvidia-settings utility is a tool for configuring the NVIDIA
 Linux graphics driver.  It operates by communicating with the NVIDIA
 X driver, querying and updating state as appropriate.  This
 communication is done with the NV-CONTROL X extension.
 .
 Values such as brightness and gamma, XVideo attributes, temperature,
 and OpenGL settings can be queried and configured via nvidia-settings.

Package: nvidia-visual-profiler
Description-md5: c762f649b112cccddd5b9e96863b94c7
Description-en: NVIDIA Visual Profiler for CUDA and OpenCL
 The NVIDIA Visual Profiler is a cross-platform performance profiling tool
 that delivers developers vital feedback for optimizing CUDA C/C++ and OpenCL
 applications.

Package: python-pycuda
Description-md5: 999e10331c8e1f852d56bf92b2010d4b
Description-en: Python module to access Nvidia‘s CUDA parallel computation API
 PyCUDA lets you access Nvidia‘s CUDA parallel computation API from Python.
 Several wrappers of the CUDA API already exist–so what’s so special about
 PyCUDA?
  * Object cleanup tied to lifetime of objects. This idiom, often called
    RAII in C++, makes it much easier to write correct, leak- and crash-free
    code.  PyCUDA knows about dependencies, too, so (for example) it won’t
    detach from a context before all memory allocated in it is also freed.
  * Convenience. Abstractions like pycuda.driver.SourceModule and
    pycuda.gpuarray.GPUArray make CUDA programming even more convenient than
    with Nvidia’s C-based runtime.
  * Completeness. PyCUDA puts the full power of CUDA’s driver API at your
    disposal, if you wish.
  * Automatic Error Checking. All CUDA errors are automatically translated
    into Python exceptions.
  * Speed. PyCUDA’s base layer is written in C++, so all the niceties
    above are virtually free.
  * Helpful Documentation.

Package: python-pycuda-dbg
Description-md5: ae08beb79e6eee13bd1e5931765375b1
Description-en: Python module to access Nvidia‘s CUDA API (debug extensions)
 PyCUDA lets you access Nvidia‘s CUDA parallel computation API from Python.
 Several wrappers of the CUDA API already exist–so what’s so special about
 PyCUDA?
  * Object cleanup tied to lifetime of objects. This idiom, often called
    RAII in C++, makes it much easier to write correct, leak- and crash-free
    code.  PyCUDA knows about dependencies, too, so (for example) it won’t
    detach from a context before all memory allocated in it is also freed.
  * Convenience. Abstractions like pycuda.driver.SourceModule and
    pycuda.gpuarray.GPUArray make CUDA programming even more convenient than
    with Nvidia’s C-based runtime.
  * Completeness. PyCUDA puts the full power of CUDA’s driver API at your
    disposal, if you wish.
  * Automatic Error Checking. All CUDA errors are automatically translated
    into Python exceptions.
  * Speed. PyCUDA’s base layer is written in C++, so all the niceties
    above are virtually free.
  * Helpful Documentation.
 .
 This package contains debug extensions build for the Python debug interpreter.

Package: python-pycuda-doc
Description-md5: 4b4f2b1e8b32879eefe98c99f3a598ba
Description-en: module to access Nvidia‘s CUDA computation API (documentation)
 PyCUDA lets you access Nvidia‘s CUDA parallel computation API from Python.
 Several wrappers of the CUDA API already exist–so what’s so special about
 PyCUDA?
  * Object cleanup tied to lifetime of objects. This idiom, often called
    RAII in C++, makes it much easier to write correct, leak- and crash-free
    code.  PyCUDA knows about dependencies, too, so (for example) it won’t
    detach from a context before all memory allocated in it is also freed.
  * Convenience. Abstractions like pycuda.driver.SourceModule and
    pycuda.gpuarray.GPUArray make CUDA programming even more convenient than
    with Nvidia’s C-based runtime.
  * Completeness. PyCUDA puts the full power of CUDA’s driver API at your
    disposal, if you wish.
  * Automatic Error Checking. All CUDA errors are automatically translated
    into Python exceptions.
  * Speed. PyCUDA’s base layer is written in C++, so all the niceties
    above are virtually free.
  * Helpful Documentation.
 .
 This package contains HTML documentation and example scripts.

Package: python3-caffe-cuda
Description-md5: 09838b3c464bba5d6ce1e2fa714afbe8
Description-en: Python3 interface of Caffe (CUDA)
 Caffe is a deep learning framework made with expression, speed,
 and modularity in mind. It is developed by the Berkeley AI Research
 Lab (BAIR) and community contributors.
 .
 It contains the CUDA version of python3 Caffe interface.

Package: python3-pycuda
Description-md5: 4f446cb70e3ba6723eaae62a94efb36c
Description-en: Python 3 module to access Nvidia‘s CUDA parallel computation API
 PyCUDA lets you access Nvidia‘s CUDA parallel computation API from Python.
 Several wrappers of the CUDA API already exist–so what’s so special about
 PyCUDA?
  * Object cleanup tied to lifetime of objects. This idiom, often called
    RAII in C++, makes it much easier to write correct, leak- and crash-free
    code.  PyCUDA knows about dependencies, too, so (for example) it won’t
    detach from a context before all memory allocated in it is also freed.
  * Convenience. Abstractions like pycuda.driver.SourceModule and
    pycuda.gpuarray.GPUArray make CUDA programming even more convenient than
    with Nvidia’s C-based runtime.
  * Completeness. PyCUDA puts the full power of CUDA’s driver API at your
    disposal, if you wish.
  * Automatic Error Checking. All CUDA errors are automatically translated
    into Python exceptions.
  * Speed. PyCUDA’s base layer is written in C++, so all the niceties
    above are virtually free.
  * Helpful Documentation.
 .
 This package contains Python 3 modules.

Package: python3-pycuda-dbg
Description-md5: 2408be5275171c7291f7d0f275b7d393
Description-en: Python 3 module to access Nvidia‘s CUDA API (debug extensions)
 PyCUDA lets you access Nvidia‘s CUDA parallel computation API from Python.
 Several wrappers of the CUDA API already exist–so what’s so special about
 PyCUDA?
  * Object cleanup tied to lifetime of objects. This idiom, often called
    RAII in C++, makes it much easier to write correct, leak- and crash-free
    code.  PyCUDA knows about dependencies, too, so (for example) it won’t
    detach from a context before all memory allocated in it is also freed.
  * Convenience. Abstractions like pycuda.driver.SourceModule and
    pycuda.gpuarray.GPUArray make CUDA programming even more convenient than
    with Nvidia’s C-based runtime.
  * Completeness. PyCUDA puts the full power of CUDA’s driver API at your
    disposal, if you wish.
  * Automatic Error Checking. All CUDA errors are automatically translated
    into Python exceptions.
  * Speed. PyCUDA’s base layer is written in C++, so all the niceties
    above are virtually free.
  * Helpful Documentation.
 .
 This package contains debug extensions for the Python 3 debug interpreter.

Package: starpu-contrib-examples
Description-md5: 11b8c6811b08d3b761c4e2f5fd0e0890
Description-en: Task scheduler for heterogeneous multicore machines - exs
 StarPU is a runtime system that offers support for heterogeneous
 multicore machines. While many efforts are devoted to design efficient
 computation kernels for those architectures (e.g. to implement BLAS
 kernels on GPUs or on Cell's SPUs), StarPU not only takes care of
 offloading such kernels (and implementing data coherency across
 the machine), but it also makes sure the kernels are executed as
 efficiently as possible.
 .
 This package contains application examples.
 This "contrib" version is built against CUDA.

Package: starpu-contrib-tools
Description-md5: 21e204c0915cd3972a9cf41ad059d147
Description-en: Task scheduler for heterogeneous multicore machines - tools
 StarPU is a runtime system that offers support for heterogeneous
 multicore machines. While many efforts are devoted to design efficient
 computation kernels for those architectures (e.g. to implement BLAS
 kernels on GPUs or on Cell's SPUs), StarPU not only takes care of
 offloading such kernels (and implementing data coherency across
 the machine), but it also makes sure the kernels are executed as
 efficiently as possible.
 .
 This package contains StarPU tools.
 This "contrib" version is built against CUDA.

Package: xserver-xorg-video-nvidia-legacy-390xx
Description-md5: 17de0107aa29de52365214304a8c6942
Description-en: NVIDIA binary Xorg driver (390xx legacy version)
 The NVIDIA binary driver provides optimized hardware acceleration of
 OpenGL/GLX/EGL/GLES applications via a direct-rendering X Server
 for graphics cards using NVIDIA chip sets.
 .
 Please see the nvidia-legacy-390xx-kernel-dkms or
 nvidia-legacy-390xx-kernel-source packages
 for building the kernel module required by this package.
 This will provide nvidia-legacy-390xx-kernel-390.87.
 .
 This legacy version is the last release that supports the following GPUs:
 GeForce 410M [GF119M], GeForce 510 [GF119], GeForce 605 [GF119],
 GeForce 610M [GF108M], GeForce 610M [GF119M], GeForce 610M [GF117M],
 GeForce 705M [GF119M], GeForce 710M [GF117M], GeForce 810M [GF117M],
 GeForce 820M [GF117M], GeForce GT 415M [GF108M], GeForce GT 420 [GF108],
 GeForce GT 420M [GF108M], GeForce GT 425M [GF108M], GeForce GT 430 [GF108],
 GeForce GT 435M [GF106M], GeForce GT 435M [GF108M], GeForce GT 440 [GF106],
 GeForce GT 440 [GF108], GeForce GT 445M [GF106M], GeForce GT 520 [GF108],
 GeForce GT 520 [GF119], GeForce GT 520M [GF108M], GeForce GT 520M [GF119M],
 GeForce GT 520MX [GF119M], GeForce GT 525M [GF108M], GeForce GT 530 [GF108],
 GeForce GT 540M [GF108M], GeForce GT 545 OEM [GF116], GeForce GT 545 [GF116],
 GeForce GT 550M [GF106M], GeForce GT 550M [GF108M], GeForce GT 550M [GF116M],
 GeForce GT 555M [GF106M], GeForce GT 555M [GF108M], GeForce GT 555M [GF116M],
 GeForce GT 560M [GF116M], GeForce GT 610 [GF108], GeForce GT 610 [GF119],
 GeForce GT 620 OEM [GF119], GeForce GT 620 [GF108], GeForce GT 620M [GF108M],
 GeForce GT 620M [GF117M], GeForce GT 620M LE [GF108M],
 GeForce GT 625 OEM [GF119], GeForce GT 625M [GF117M], GeForce GT 630 [GF108],
 GeForce GT 630M [GF117M], GeForce GT 630M LE [GF108M],
 GeForce GT 635M [GF108M], GeForce GT 635M [GF116M],
 GeForce GT 635M LE [GF108M], GeForce GT 640M LE [GF108M],
 GeForce GT 640 OEM [GF116], GeForce GT 645 OEM [GF114],
 GeForce GT 705 [GF119], GeForce GT 720M [GF117M], GeForce GT 730 [GF108],
 GeForce GTS 450 OEM [GF106], GeForce GTS 450 [GF106],
 GeForce GTS 450 Rev. 2 [GF116], GeForce GTS 450 Rev. 3 [GF116],
 GeForce GTX 460 OEM [GF104], GeForce GTX 460 [GF104],
 GeForce GTX 460 v2 [GF114], GeForce GTX 460 SE [GF104],
 GeForce GTX 460 SE v2 [GF114], GeForce GTX 460M [GF106M],
 GeForce GTX 465 [GF100], GeForce GTX 470 [GF100], GeForce GTX 470M [GF104M],
 GeForce GTX 480 [GF100], GeForce GTX 480M [GF100M],
 GeForce GTX 485M [GF104M], GeForce GTX 550 Ti [GF116],
 GeForce GTX 555 [GF114], GeForce GTX 560 OEM [GF110],
 GeForce GTX 560 [GF114], GeForce GTX 560 SE [GF114],
 GeForce GTX 560 Ti [GF114], GeForce GTX 560 Ti OEM [GF110],
 GeForce GTX 560 Ti 448 Cores [GF110], GeForce GTX 570 [GF110],
 GeForce GTX 570 Rev. 2 [GF110], GeForce GTX 570M [GF114M],
 GeForce GTX 580 [GF110], GeForce GTX 580 Rev. 2 [GF110],
 GeForce GTX 580M [GF114M], GeForce GTX 590 [GF110],
 GeForce GTX 670M [GF114M], GeForce GTX 675M [GF114M], NVS 310 [GF119],
 NVS 315 [GF119], NVS 4200M [GF119M], NVS 5200M [GF108GLM],
 NVS 5400M [GF108M], Quadro 500M [GF108GLM], Quadro 600 [GF108GL],
 Quadro 1000M [GF108GLM], Quadro 2000 [GF106GL], Quadro 2000M [GF106GLM],
 Quadro 3000M [GF104GLM], Quadro 4000 [GF100GL], Quadro 4000M [GF104GLM],
 Quadro 5000 [GF100GL], Quadro 5000M [GF100GLM], Quadro 5010M [GF100GLM],
 Quadro 6000 [GF100GL], Quadro 7000 [GF100GL], Quadro NVS 4200M [GF119M],
 Tesla C2050 [GF100GL], Tesla C2050 [GF110GL], Tesla C2070 [GF100GL],
 Tesla C2075 [GF110GL], Tesla M2070 [GF100GL], Tesla M2070-Q [GF100GL],
 Tesla M2075 [GF110GL], Tesla M2090 [GF110GL], Tesla T20 Processor [GF100GL].
 .
 There are several "more modern" GPUs supported by this package, too, but the
 updated drivers in the newer legacy packages or the current nvidia-driver
 package usually provide more features and better support.
 Look at the other legacy packages for older cards.
 .
 See /usr/share/doc/nvidia-legacy-390xx-driver/README.txt.gz
 for a complete list of supported GPUs and PCI IDs.

Package: nvidia-legacy-390xx-kernel-dkms
Description-md5: ad743e2641c6695280c3af3ca8a5b695
Description-en: NVIDIA binary kernel module DKMS source (390xx legacy version)
 This package builds the NVIDIA Xorg binary kernel module needed by
 nvidia-legacy-390xx-driver, using DKMS.
 Provided that you have the kernel header packages installed, the kernel
 module will be built for your running kernel and automatically rebuilt for
 any new kernel headers that are installed.
 .
 The NVIDIA binary driver provides optimized hardware acceleration of
 OpenGL/GLX/EGL/GLES applications via a direct-rendering X Server
 for graphics cards using NVIDIA chip sets.
 .
 This legacy version is the last release that supports the following GPUs:
 GeForce 410M [GF119M], GeForce 510 [GF119], GeForce 605 [GF119],
 GeForce 610M [GF108M], GeForce 610M [GF119M], GeForce 610M [GF117M],
 GeForce 705M [GF119M], GeForce 710M [GF117M], GeForce 810M [GF117M],
 GeForce 820M [GF117M], GeForce GT 415M [GF108M], GeForce GT 420 [GF108],
 GeForce GT 420M [GF108M], GeForce GT 425M [GF108M], GeForce GT 430 [GF108],
 GeForce GT 435M [GF106M], GeForce GT 435M [GF108M], GeForce GT 440 [GF106],
 GeForce GT 440 [GF108], GeForce GT 445M [GF106M], GeForce GT 520 [GF108],
 GeForce GT 520 [GF119], GeForce GT 520M [GF108M], GeForce GT 520M [GF119M],
 GeForce GT 520MX [GF119M], GeForce GT 525M [GF108M], GeForce GT 530 [GF108],
 GeForce GT 540M [GF108M], GeForce GT 545 OEM [GF116], GeForce GT 545 [GF116],
 GeForce GT 550M [GF106M], GeForce GT 550M [GF108M], GeForce GT 550M [GF116M],
 GeForce GT 555M [GF106M], GeForce GT 555M [GF108M], GeForce GT 555M [GF116M],
 GeForce GT 560M [GF116M], GeForce GT 610 [GF108], GeForce GT 610 [GF119],
 GeForce GT 620 OEM [GF119], GeForce GT 620 [GF108], GeForce GT 620M [GF108M],
 GeForce GT 620M [GF117M], GeForce GT 620M LE [GF108M],
 GeForce GT 625 OEM [GF119], GeForce GT 625M [GF117M], GeForce GT 630 [GF108],
 GeForce GT 630M [GF117M], GeForce GT 630M LE [GF108M],
 GeForce GT 635M [GF108M], GeForce GT 635M [GF116M],
 GeForce GT 635M LE [GF108M], GeForce GT 640M LE [GF108M],
 GeForce GT 640 OEM [GF116], GeForce GT 645 OEM [GF114],
 GeForce GT 705 [GF119], GeForce GT 720M [GF117M], GeForce GT 730 [GF108],
 GeForce GTS 450 OEM [GF106], GeForce GTS 450 [GF106],
 GeForce GTS 450 Rev. 2 [GF116], GeForce GTS 450 Rev. 3 [GF116],
 GeForce GTX 460 OEM [GF104], GeForce GTX 460 [GF104],
 GeForce GTX 460 v2 [GF114], GeForce GTX 460 SE [GF104],
 GeForce GTX 460 SE v2 [GF114], GeForce GTX 460M [GF106M],
 GeForce GTX 465 [GF100], GeForce GTX 470 [GF100], GeForce GTX 470M [GF104M],
 GeForce GTX 480 [GF100], GeForce GTX 480M [GF100M],
 GeForce GTX 485M [GF104M], GeForce GTX 550 Ti [GF116],
 GeForce GTX 555 [GF114], GeForce GTX 560 OEM [GF110],
 GeForce GTX 560 [GF114], GeForce GTX 560 SE [GF114],
 GeForce GTX 560 Ti [GF114], GeForce GTX 560 Ti OEM [GF110],
 GeForce GTX 560 Ti 448 Cores [GF110], GeForce GTX 570 [GF110],
 GeForce GTX 570 Rev. 2 [GF110], GeForce GTX 570M [GF114M],
 GeForce GTX 580 [GF110], GeForce GTX 580 Rev. 2 [GF110],
 GeForce GTX 580M [GF114M], GeForce GTX 590 [GF110],
 GeForce GTX 670M [GF114M], GeForce GTX 675M [GF114M], NVS 310 [GF119],
 NVS 315 [GF119], NVS 4200M [GF119M], NVS 5200M [GF108GLM],
 NVS 5400M [GF108M], Quadro 500M [GF108GLM], Quadro 600 [GF108GL],
 Quadro 1000M [GF108GLM], Quadro 2000 [GF106GL], Quadro 2000M [GF106GLM],
 Quadro 3000M [GF104GLM], Quadro 4000 [GF100GL], Quadro 4000M [GF104GLM],
 Quadro 5000 [GF100GL], Quadro 5000M [GF100GLM], Quadro 5010M [GF100GLM],
 Quadro 6000 [GF100GL], Quadro 7000 [GF100GL], Quadro NVS 4200M [GF119M],
 Tesla C2050 [GF100GL], Tesla C2050 [GF110GL], Tesla C2070 [GF100GL],
 Tesla C2075 [GF110GL], Tesla M2070 [GF100GL], Tesla M2070-Q [GF100GL],
 Tesla M2075 [GF110GL], Tesla M2090 [GF110GL], Tesla T20 Processor [GF100GL].
 .
 There are several "more modern" GPUs supported by this package, too, but the
 updated drivers in the newer legacy packages or the current nvidia-driver
 package usually provide more features and better support.
 Look at the other legacy packages for older cards.
 .
 See /usr/share/doc/nvidia-legacy-390xx-kernel-dkms/README.txt.gz
 for a complete list of supported GPUs and PCI IDs.
 .
 This package contains the blobs for building kernel modules for the
 armhf architecture.
 Building the kernel modules has been tested up to Linux 4.20.

Package: nvidia-legacy-390xx-kernel-source
Description-md5: ae417a1b61940029f3bfa4a48fcbd242
Description-en: NVIDIA binary kernel module source (390xx legacy version)
 This package provides the source for the NVIDIA Xorg binary kernel module
 needed by nvidia-legacy-390xx-driver in a form suitable
 for use by module-assistant or kernel-package.
 .
 The NVIDIA binary driver provides optimized hardware acceleration of
 OpenGL/GLX/EGL/GLES applications via a direct-rendering X Server
 for graphics cards using NVIDIA chip sets.
 .
 PLEASE read /usr/share/doc/nvidia-legacy-390xx-kernel-source/README.Debian.gz
 for building information.  If you want the kernel module to be automatically
 installed via DKMS, install nvidia-legacy-390xx-kernel-dkms instead.
 .
 This legacy version is the last release that supports the following GPUs:
 GeForce 410M [GF119M], GeForce 510 [GF119], GeForce 605 [GF119],
 GeForce 610M [GF108M], GeForce 610M [GF119M], GeForce 610M [GF117M],
 GeForce 705M [GF119M], GeForce 710M [GF117M], GeForce 810M [GF117M],
 GeForce 820M [GF117M], GeForce GT 415M [GF108M], GeForce GT 420 [GF108],
 GeForce GT 420M [GF108M], GeForce GT 425M [GF108M], GeForce GT 430 [GF108],
 GeForce GT 435M [GF106M], GeForce GT 435M [GF108M], GeForce GT 440 [GF106],
 GeForce GT 440 [GF108], GeForce GT 445M [GF106M], GeForce GT 520 [GF108],
 GeForce GT 520 [GF119], GeForce GT 520M [GF108M], GeForce GT 520M [GF119M],
 GeForce GT 520MX [GF119M], GeForce GT 525M [GF108M], GeForce GT 530 [GF108],
 GeForce GT 540M [GF108M], GeForce GT 545 OEM [GF116], GeForce GT 545 [GF116],
 GeForce GT 550M [GF106M], GeForce GT 550M [GF108M], GeForce GT 550M [GF116M],
 GeForce GT 555M [GF106M], GeForce GT 555M [GF108M], GeForce GT 555M [GF116M],
 GeForce GT 560M [GF116M], GeForce GT 610 [GF108], GeForce GT 610 [GF119],
 GeForce GT 620 OEM [GF119], GeForce GT 620 [GF108], GeForce GT 620M [GF108M],
 GeForce GT 620M [GF117M], GeForce GT 620M LE [GF108M],
 GeForce GT 625 OEM [GF119], GeForce GT 625M [GF117M], GeForce GT 630 [GF108],
 GeForce GT 630M [GF117M], GeForce GT 630M LE [GF108M],
 GeForce GT 635M [GF108M], GeForce GT 635M [GF116M],
 GeForce GT 635M LE [GF108M], GeForce GT 640M LE [GF108M],
 GeForce GT 640 OEM [GF116], GeForce GT 645 OEM [GF114],
 GeForce GT 705 [GF119], GeForce GT 720M [GF117M], GeForce GT 730 [GF108],
 GeForce GTS 450 OEM [GF106], GeForce GTS 450 [GF106],
 GeForce GTS 450 Rev. 2 [GF116], GeForce GTS 450 Rev. 3 [GF116],
 GeForce GTX 460 OEM [GF104], GeForce GTX 460 [GF104],
 GeForce GTX 460 v2 [GF114], GeForce GTX 460 SE [GF104],
 GeForce GTX 460 SE v2 [GF114], GeForce GTX 460M [GF106M],
 GeForce GTX 465 [GF100], GeForce GTX 470 [GF100], GeForce GTX 470M [GF104M],
 GeForce GTX 480 [GF100], GeForce GTX 480M [GF100M],
 GeForce GTX 485M [GF104M], GeForce GTX 550 Ti [GF116],
 GeForce GTX 555 [GF114], GeForce GTX 560 OEM [GF110],
 GeForce GTX 560 [GF114], GeForce GTX 560 SE [GF114],
 GeForce GTX 560 Ti [GF114], GeForce GTX 560 Ti OEM [GF110],
 GeForce GTX 560 Ti 448 Cores [GF110], GeForce GTX 570 [GF110],
 GeForce GTX 570 Rev. 2 [GF110], GeForce GTX 570M [GF114M],
 GeForce GTX 580 [GF110], GeForce GTX 580 Rev. 2 [GF110],
 GeForce GTX 580M [GF114M], GeForce GTX 590 [GF110],
 GeForce GTX 670M [GF114M], GeForce GTX 675M [GF114M], NVS 310 [GF119],
 NVS 315 [GF119], NVS 4200M [GF119M], NVS 5200M [GF108GLM],
 NVS 5400M [GF108M], Quadro 500M [GF108GLM], Quadro 600 [GF108GL],
 Quadro 1000M [GF108GLM], Quadro 2000 [GF106GL], Quadro 2000M [GF106GLM],
 Quadro 3000M [GF104GLM], Quadro 4000 [GF100GL], Quadro 4000M [GF104GLM],
 Quadro 5000 [GF100GL], Quadro 5000M [GF100GLM], Quadro 5010M [GF100GLM],
 Quadro 6000 [GF100GL], Quadro 7000 [GF100GL], Quadro NVS 4200M [GF119M],
 Tesla C2050 [GF100GL], Tesla C2050 [GF110GL], Tesla C2070 [GF100GL],
 Tesla C2075 [GF110GL], Tesla M2070 [GF100GL], Tesla M2070-Q [GF100GL],
 Tesla M2075 [GF110GL], Tesla M2090 [GF110GL], Tesla T20 Processor [GF100GL].
 .
 There are several "more modern" GPUs supported by this package, too, but the
 updated drivers in the newer legacy packages or the current nvidia-driver
 package usually provide more features and better support.
 Look at the other legacy packages for older cards.
 .
 See /usr/share/doc/nvidia-legacy-390xx-kernel-source/README.txt.gz
 for a complete list of supported GPUs and PCI IDs.
 .
 This package contains the blobs for building kernel modules for the
 armhf architecture.
 Building the kernel modules has been tested up to Linux 4.20.

Package: libnvidia-legacy-390xx-cuda1-i386
Description-md5: f39b2aebe877722a81b97212f84bb0ee
Description-en: NVIDIA CUDA 32-bit runtime library (390xx legacy version)
 This metapackage helps the automatic installation of the 32-bit NVIDIA CUDA
 library when installing libnvidia-legacy-390xx-cuda1 on amd64 with foreign
 architecture i386 enabled.

Package: nvidia-legacy-390xx-driver-libs-i386
Description-md5: 3bb1065d7c554282e7f0df6304528ad8
Description-en: NVIDIA metapackage (OpenGL/GLX/EGL/GLES 32-bit libraries) (390xx legacy)
 This metapackage helps the automatic installation of the 32-bit NVIDIA
 OpenGL/GLX/EGL/GLES libraries when installing nvidia-legacy-390xx-driver-libs
 on amd64 with foreign architecture i386 enabled.

Package: nvidia-legacy-390xx-driver-libs-nonglvnd-i386
Description-md5: 4edf7f57943b451eaa1f52525b82ffc5
Description-en: NVIDIA metapackage (non-GLVND OpenGL/EGL/etc. 32-bit libraries) (390xx legacy)
 This metapackage helps the automatic installation of the 32-bit NVIDIA
 OpenGL/GLX/EGL/GLES libraries when installing
 nvidia-legacy-390xx-driver-libs-nonglvnd
 on amd64 with foreign architecture i386 enabled.

Package: nvidia-legacy-390xx-kernel-dkms
Description-md5: 2f9a618a15db4c24a1323a4efa246dbc
Description-en: NVIDIA binary kernel module DKMS source (390xx legacy version)
 This package builds the NVIDIA Xorg binary kernel module needed by
 nvidia-legacy-390xx-driver, using DKMS.
 Provided that you have the kernel header packages installed, the kernel
 module will be built for your running kernel and automatically rebuilt for
 any new kernel headers that are installed.
 .
 The NVIDIA binary driver provides optimized hardware acceleration of
 OpenGL/GLX/EGL/GLES applications via a direct-rendering X Server
 for graphics cards using NVIDIA chip sets.
 .
 This legacy version is the last release that supports the following GPUs:
 GeForce 410M [GF119M], GeForce 510 [GF119], GeForce 605 [GF119],
 GeForce 610M [GF108M], GeForce 610M [GF119M], GeForce 610M [GF117M],
 GeForce 705M [GF119M], GeForce 710M [GF117M], GeForce 810M [GF117M],
 GeForce 820M [GF117M], GeForce GT 415M [GF108M], GeForce GT 420 [GF108],
 GeForce GT 420M [GF108M], GeForce GT 425M [GF108M], GeForce GT 430 [GF108],
 GeForce GT 435M [GF106M], GeForce GT 435M [GF108M], GeForce GT 440 [GF106],
 GeForce GT 440 [GF108], GeForce GT 445M [GF106M], GeForce GT 520 [GF108],
 GeForce GT 520 [GF119], GeForce GT 520M [GF108M], GeForce GT 520M [GF119M],
 GeForce GT 520MX [GF119M], GeForce GT 525M [GF108M], GeForce GT 530 [GF108],
 GeForce GT 540M [GF108M], GeForce GT 545 OEM [GF116], GeForce GT 545 [GF116],
 GeForce GT 550M [GF106M], GeForce GT 550M [GF108M], GeForce GT 550M [GF116M],
 GeForce GT 555M [GF106M], GeForce GT 555M [GF108M], GeForce GT 555M [GF116M],
 GeForce GT 560M [GF116M], GeForce GT 610 [GF108], GeForce GT 610 [GF119],
 GeForce GT 620 OEM [GF119], GeForce GT 620 [GF108], GeForce GT 620M [GF108M],
 GeForce GT 620M [GF117M], GeForce GT 620M LE [GF108M],
 GeForce GT 625 OEM [GF119], GeForce GT 625M [GF117M], GeForce GT 630 [GF108],
 GeForce GT 630M [GF117M], GeForce GT 630M LE [GF108M],
 GeForce GT 635M [GF108M], GeForce GT 635M [GF116M],
 GeForce GT 635M LE [GF108M], GeForce GT 640M LE [GF108M],
 GeForce GT 640 OEM [GF116], GeForce GT 645 OEM [GF114],
 GeForce GT 705 [GF119], GeForce GT 720M [GF117M], GeForce GT 730 [GF108],
 GeForce GTS 450 OEM [GF106], GeForce GTS 450 [GF106],
 GeForce GTS 450 Rev. 2 [GF116], GeForce GTS 450 Rev. 3 [GF116],
 GeForce GTX 460 OEM [GF104], GeForce GTX 460 [GF104],
 GeForce GTX 460 v2 [GF114], GeForce GTX 460 SE [GF104],
 GeForce GTX 460 SE v2 [GF114], GeForce GTX 460M [GF106M],
 GeForce GTX 465 [GF100], GeForce GTX 470 [GF100], GeForce GTX 470M [GF104M],
 GeForce GTX 480 [GF100], GeForce GTX 480M [GF100M],
 GeForce GTX 485M [GF104M], GeForce GTX 550 Ti [GF116],
 GeForce GTX 555 [GF114], GeForce GTX 560 OEM [GF110],
 GeForce GTX 560 [GF114], GeForce GTX 560 SE [GF114],
 GeForce GTX 560 Ti [GF114], GeForce GTX 560 Ti OEM [GF110],
 GeForce GTX 560 Ti 448 Cores [GF110], GeForce GTX 570 [GF110],
 GeForce GTX 570 Rev. 2 [GF110], GeForce GTX 570M [GF114M],
 GeForce GTX 580 [GF110], GeForce GTX 580 Rev. 2 [GF110],
 GeForce GTX 580M [GF114M], GeForce GTX 590 [GF110],
 GeForce GTX 670M [GF114M], GeForce GTX 675M [GF114M], NVS 310 [GF119],
 NVS 315 [GF119], NVS 4200M [GF119M], NVS 5200M [GF108GLM],
 NVS 5400M [GF108M], Quadro 500M [GF108GLM], Quadro 600 [GF108GL],
 Quadro 1000M [GF108GLM], Quadro 2000 [GF106GL], Quadro 2000M [GF106GLM],
 Quadro 3000M [GF104GLM], Quadro 4000 [GF100GL], Quadro 4000M [GF104GLM],
 Quadro 5000 [GF100GL], Quadro 5000M [GF100GLM], Quadro 5010M [GF100GLM],
 Quadro 6000 [GF100GL], Quadro 7000 [GF100GL], Quadro NVS 4200M [GF119M],
 Tesla C2050 [GF100GL], Tesla C2050 [GF110GL], Tesla C2070 [GF100GL],
 Tesla C2075 [GF110GL], Tesla M2070 [GF100GL], Tesla M2070-Q [GF100GL],
 Tesla M2075 [GF110GL], Tesla M2090 [GF110GL], Tesla T20 Processor [GF100GL].
 .
 There are several "more modern" GPUs supported by this package, too, but the
 updated drivers in the newer legacy packages or the current nvidia-driver
 package usually provide more features and better support.
 Look at the other legacy packages for older cards.
 .
 See /usr/share/doc/nvidia-legacy-390xx-kernel-dkms/README.txt.gz
 for a complete list of supported GPUs and PCI IDs.
 .
 This package contains the blobs for building kernel modules for the
 i386 architecture.
 Building the kernel modules has been tested up to Linux 4.20.

Package: nvidia-legacy-390xx-kernel-source
Description-md5: d0b362bd0745471ad160ca3cb98c8f90
Description-en: NVIDIA binary kernel module source (390xx legacy version)
 This package provides the source for the NVIDIA Xorg binary kernel module
 needed by nvidia-legacy-390xx-driver in a form suitable
 for use by module-assistant or kernel-package.
 .
 The NVIDIA binary driver provides optimized hardware acceleration of
 OpenGL/GLX/EGL/GLES applications via a direct-rendering X Server
 for graphics cards using NVIDIA chip sets.
 .
 PLEASE read /usr/share/doc/nvidia-legacy-390xx-kernel-source/README.Debian.gz
 for building information.  If you want the kernel module to be automatically
 installed via DKMS, install nvidia-legacy-390xx-kernel-dkms instead.
 .
 This legacy version is the last release that supports the following GPUs:
 GeForce 410M [GF119M], GeForce 510 [GF119], GeForce 605 [GF119],
 GeForce 610M [GF108M], GeForce 610M [GF119M], GeForce 610M [GF117M],
 GeForce 705M [GF119M], GeForce 710M [GF117M], GeForce 810M [GF117M],
 GeForce 820M [GF117M], GeForce GT 415M [GF108M], GeForce GT 420 [GF108],
 GeForce GT 420M [GF108M], GeForce GT 425M [GF108M], GeForce GT 430 [GF108],
 GeForce GT 435M [GF106M], GeForce GT 435M [GF108M], GeForce GT 440 [GF106],
 GeForce GT 440 [GF108], GeForce GT 445M [GF106M], GeForce GT 520 [GF108],
 GeForce GT 520 [GF119], GeForce GT 520M [GF108M], GeForce GT 520M [GF119M],
 GeForce GT 520MX [GF119M], GeForce GT 525M [GF108M], GeForce GT 530 [GF108],
 GeForce GT 540M [GF108M], GeForce GT 545 OEM [GF116], GeForce GT 545 [GF116],
 GeForce GT 550M [GF106M], GeForce GT 550M [GF108M], GeForce GT 550M [GF116M],
 GeForce GT 555M [GF106M], GeForce GT 555M [GF108M], GeForce GT 555M [GF116M],
 GeForce GT 560M [GF116M], GeForce GT 610 [GF108], GeForce GT 610 [GF119],
 GeForce GT 620 OEM [GF119], GeForce GT 620 [GF108], GeForce GT 620M [GF108M],
 GeForce GT 620M [GF117M], GeForce GT 620M LE [GF108M],
 GeForce GT 625 OEM [GF119], GeForce GT 625M [GF117M], GeForce GT 630 [GF108],
 GeForce GT 630M [GF117M], GeForce GT 630M LE [GF108M],
 GeForce GT 635M [GF108M], GeForce GT 635M [GF116M],
 GeForce GT 635M LE [GF108M], GeForce GT 640M LE [GF108M],
 GeForce GT 640 OEM [GF116], GeForce GT 645 OEM [GF114],
 GeForce GT 705 [GF119], GeForce GT 720M [GF117M], GeForce GT 730 [GF108],
 GeForce GTS 450 OEM [GF106], GeForce GTS 450 [GF106],
 GeForce GTS 450 Rev. 2 [GF116], GeForce GTS 450 Rev. 3 [GF116],
 GeForce GTX 460 OEM [GF104], GeForce GTX 460 [GF104],
 GeForce GTX 460 v2 [GF114], GeForce GTX 460 SE [GF104],
 GeForce GTX 460 SE v2 [GF114], GeForce GTX 460M [GF106M],
 GeForce GTX 465 [GF100], GeForce GTX 470 [GF100], GeForce GTX 470M [GF104M],
 GeForce GTX 480 [GF100], GeForce GTX 480M [GF100M],
 GeForce GTX 485M [GF104M], GeForce GTX 550 Ti [GF116],
 GeForce GTX 555 [GF114], GeForce GTX 560 OEM [GF110],
 GeForce GTX 560 [GF114], GeForce GTX 560 SE [GF114],
 GeForce GTX 560 Ti [GF114], GeForce GTX 560 Ti OEM [GF110],
 GeForce GTX 560 Ti 448 Cores [GF110], GeForce GTX 570 [GF110],
 GeForce GTX 570 Rev. 2 [GF110], GeForce GTX 570M [GF114M],
 GeForce GTX 580 [GF110], GeForce GTX 580 Rev. 2 [GF110],
 GeForce GTX 580M [GF114M], GeForce GTX 590 [GF110],
 GeForce GTX 670M [GF114M], GeForce GTX 675M [GF114M], NVS 310 [GF119],
 NVS 315 [GF119], NVS 4200M [GF119M], NVS 5200M [GF108GLM],
 NVS 5400M [GF108M], Quadro 500M [GF108GLM], Quadro 600 [GF108GL],
 Quadro 1000M [GF108GLM], Quadro 2000 [GF106GL], Quadro 2000M [GF106GLM],
 Quadro 3000M [GF104GLM], Quadro 4000 [GF100GL], Quadro 4000M [GF104GLM],
 Quadro 5000 [GF100GL], Quadro 5000M [GF100GLM], Quadro 5010M [GF100GLM],
 Quadro 6000 [GF100GL], Quadro 7000 [GF100GL], Quadro NVS 4200M [GF119M],
 Tesla C2050 [GF100GL], Tesla C2050 [GF110GL], Tesla C2070 [GF100GL],
 Tesla C2075 [GF110GL], Tesla M2070 [GF100GL], Tesla M2070-Q [GF100GL],
 Tesla M2075 [GF110GL], Tesla M2090 [GF110GL], Tesla T20 Processor [GF100GL].
 .
 There are several "more modern" GPUs supported by this package, too, but the
 updated drivers in the newer legacy packages or the current nvidia-driver
 package usually provide more features and better support.
 Look at the other legacy packages for older cards.
 .
 See /usr/share/doc/nvidia-legacy-390xx-kernel-source/README.txt.gz
 for a complete list of supported GPUs and PCI IDs.
 .
 This package contains the blobs for building kernel modules for the
 i386 architecture.
 Building the kernel modules has been tested up to Linux 4.20.