Skip to main content

Accera GPU Support

Project description

Accera GPU

Accera

Accera is a programming model, a domain-specific programming language embedded in Python (eDSL), and an optimizing cross-compiler for compute-intensive code. Accera currently supports CPU and GPU targets and focuses on optimization of nested for-loops.

Writing highly optimized compute-intensive code in a traditional programming language is a difficult and time-consuming process. It requires special engineering skills, such as fluency in Assembly language and a deep understanding of computer architecture. Manually optimizing the simplest numerical algorithms already requires a significant engineering effort. Moreover, highly optimized numerical code is prone to bugs, is often hard to read and maintain, and needs to be reimplemented every time a new target architecture is introduced. Accera aims to solve these problems.

Accera has three goals:

  • Performance: generate the fastest implementation of any compute-intensive algorithm.
  • Readability: do so without sacrificing code readability and maintainability.
  • Writability: a user-friendly programming model, designed for agility.

accera-gpu

The accera-gpu package contains add-ons for GPU support. You can find documentation and examples on Github.

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

accera_gpu-1.2.26-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (36.5 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ x86-64

accera_gpu-1.2.26-cp310-cp310-macosx_11_0_x86_64.whl (39.3 MB view details)

Uploaded CPython 3.10 macOS 11.0+ x86-64

accera_gpu-1.2.26-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (36.5 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ x86-64

accera_gpu-1.2.26-cp39-cp39-macosx_11_0_x86_64.whl (39.3 MB view details)

Uploaded CPython 3.9 macOS 11.0+ x86-64

accera_gpu-1.2.26-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (36.5 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ x86-64

accera_gpu-1.2.26-cp38-cp38-macosx_10_15_x86_64.whl (39.3 MB view details)

Uploaded CPython 3.8 macOS 10.15+ x86-64

accera_gpu-1.2.26-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (36.5 MB view details)

Uploaded CPython 3.7m manylinux: glibc 2.17+ x86-64

accera_gpu-1.2.26-cp37-cp37m-macosx_10_15_x86_64.whl (39.3 MB view details)

Uploaded CPython 3.7m macOS 10.15+ x86-64

File details

Details for the file accera_gpu-1.2.26-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for accera_gpu-1.2.26-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 b465f30c651bf2645e89c876e3d4c3d29be4fcf96ce10403c53052c1b556dc76
MD5 fe56236012a60b56af5a34c4ff62154d
BLAKE2b-256 1e3f29cc387fb7f95136c4b5f533d676e130d17c031ba5b9c2614c664d68abf3

See more details on using hashes here.

File details

Details for the file accera_gpu-1.2.26-cp310-cp310-macosx_11_0_x86_64.whl.

File metadata

File hashes

Hashes for accera_gpu-1.2.26-cp310-cp310-macosx_11_0_x86_64.whl
Algorithm Hash digest
SHA256 8b68dd8e6325a6c2c02411165c2a21c43551c0e2e0dfe790a3530bcee5ee5ebb
MD5 2140f1d9879dec715253b390d41a4437
BLAKE2b-256 cd70209918400e7275d46c524624b3115640affcbc553e961cf122ccb1eda198

See more details on using hashes here.

File details

Details for the file accera_gpu-1.2.26-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for accera_gpu-1.2.26-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 f18c6c0a5116c5516a97ca9ec280870630adb4f2c4ce3bf7ccfe9213ef5bcce6
MD5 8aeffe1b58b2d9db9c12820aa19fb28b
BLAKE2b-256 623bf98f188912e49c06a6d7cb518a7c838b2d47d9bd10e6d7da7f6263d179ab

See more details on using hashes here.

File details

Details for the file accera_gpu-1.2.26-cp39-cp39-macosx_11_0_x86_64.whl.

File metadata

File hashes

Hashes for accera_gpu-1.2.26-cp39-cp39-macosx_11_0_x86_64.whl
Algorithm Hash digest
SHA256 6d09a666573aa4111f67faaa851b9f7ea00f9eb29a23ea025baaed67196eb3d8
MD5 a290b1f7b95554d76b555504a320381e
BLAKE2b-256 1198ba56ce7070f40505cd5a647086bafb19b31cb004c8db8cc00a930f9c4690

See more details on using hashes here.

File details

Details for the file accera_gpu-1.2.26-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for accera_gpu-1.2.26-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 34b246c531945b077f198844984e01e235979fa31f44c542ec85d9bcd191995e
MD5 220c1222578cc0a58460708c6d246c9d
BLAKE2b-256 9b22e7d2ebdb25e6adc8a7cfe92368784bf82a2477babe68f4fb571921abbfe1

See more details on using hashes here.

File details

Details for the file accera_gpu-1.2.26-cp38-cp38-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for accera_gpu-1.2.26-cp38-cp38-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 497ec18b7463a3f15474762e29204610c1278bd07acdf5bc7fac303953ee37bc
MD5 bc50b0825ff4530b691aa33d5677f470
BLAKE2b-256 2ccc69651289bb76ce0d973cd04a2d5168d724f33a016ebda048dade3d11854e

See more details on using hashes here.

File details

Details for the file accera_gpu-1.2.26-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for accera_gpu-1.2.26-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 dd0a373c31cd2c469934ac6d4a9f7fa58eab4e887a226fbd957cde1864b19264
MD5 54187cedecb9f9bb109a9e18e96e08aa
BLAKE2b-256 04be22bdd1e0956a21437077359d79b44f6dd6e21eb2ebf33762aca5626262ba

See more details on using hashes here.

File details

Details for the file accera_gpu-1.2.26-cp37-cp37m-macosx_10_15_x86_64.whl.

File metadata

File hashes

Hashes for accera_gpu-1.2.26-cp37-cp37m-macosx_10_15_x86_64.whl
Algorithm Hash digest
SHA256 c723ec66e1494653e437fdd4e8abdfc26316b8167a54fb50db6bd8c1b60a441f
MD5 855348058cbbc0e8e743ccd49bf028d2
BLAKE2b-256 0ea82741c2fc1ebeefa752aec522208002fd77451bdb2100e3b4d5afac05d972

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page