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-1.2.13-cp310-cp310-win_amd64.whl (7.3 MB view details)

Uploaded CPython 3.10 Windows x86-64

accera_gpu-1.2.13-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (36.4 MB view details)

Uploaded CPython 3.10 manylinux: glibc 2.17+ x86-64

accera_gpu-1.2.13-cp39-cp39-win_amd64.whl (7.3 MB view details)

Uploaded CPython 3.9 Windows x86-64

accera_gpu-1.2.13-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (36.4 MB view details)

Uploaded CPython 3.9 manylinux: glibc 2.17+ x86-64

accera_gpu-1.2.13-cp38-cp38-win_amd64.whl (7.3 MB view details)

Uploaded CPython 3.8 Windows x86-64

accera_gpu-1.2.13-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (36.4 MB view details)

Uploaded CPython 3.8 manylinux: glibc 2.17+ x86-64

accera_gpu-1.2.13-cp37-cp37m-win_amd64.whl (7.3 MB view details)

Uploaded CPython 3.7m Windows x86-64

accera_gpu-1.2.13-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (36.4 MB view details)

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

File details

Details for the file accera_gpu-1.2.13-cp310-cp310-win_amd64.whl.

File metadata

File hashes

Hashes for accera_gpu-1.2.13-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 2d87badfa84d0bd35f27249ba78e9be74f90cf28ec09ebd2621067edae3478f2
MD5 7be7f4479567e6d319a56794dc11eddd
BLAKE2b-256 a790d8a9586a173c0e09705d9d91c167eb00e512859efb908cebc8fbf221c26d

See more details on using hashes here.

File details

Details for the file accera_gpu-1.2.13-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for accera_gpu-1.2.13-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 e29b375af517ffcd98e5425c6380d259414621f11cdcbc1009b2bffb13dc1bc6
MD5 e183145c456b2d234bc7d6158b907cdb
BLAKE2b-256 89e10115db4087fa811b9bff605d8c22f213e9efbe3a2b72a67a5c5ac2a5a91c

See more details on using hashes here.

File details

Details for the file accera_gpu-1.2.13-cp39-cp39-win_amd64.whl.

File metadata

File hashes

Hashes for accera_gpu-1.2.13-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 d8888b4ea2d417edc90d6ad9a642bd8d510bb9f122156f2169844ef2faa13458
MD5 17dc79ad6e044e5fe31f0f85089824cf
BLAKE2b-256 dbeab28c8cf829b6acc8da6a4a8668e90201af5a1409462334c68f0b707ed99b

See more details on using hashes here.

File details

Details for the file accera_gpu-1.2.13-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for accera_gpu-1.2.13-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 b1fb7e741dba0136d6c998bd048fb72cbcd9294d3b883a7fa274c1e0ba5ba09b
MD5 24e7b9fcc7b9c21144ae020a0a1ee941
BLAKE2b-256 6234272c970134774aa2a6512d596dc95ba34aa20fb77106f641d7018c603291

See more details on using hashes here.

File details

Details for the file accera_gpu-1.2.13-cp38-cp38-win_amd64.whl.

File metadata

File hashes

Hashes for accera_gpu-1.2.13-cp38-cp38-win_amd64.whl
Algorithm Hash digest
SHA256 553e0258222cb64756d22d962e1e8329ac604bd754e66db6ae941e6df2cfed9c
MD5 40e152561c4580e707297a2e9214e3fb
BLAKE2b-256 ff2956b7d7c8094838d704c2d15f961d79dcf89d2365eff50c53a14c739fe6ce

See more details on using hashes here.

File details

Details for the file accera_gpu-1.2.13-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for accera_gpu-1.2.13-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 221e229ace7639d5c9da18d7834675282754cbbf214a53d3c0aeb686d26d29cd
MD5 1b1ce7800874e83870a103b9dd3a518f
BLAKE2b-256 f7b400230d3c271fdc7a57692eb7d9a5b0f69a16bfca675f7bee6642744b265f

See more details on using hashes here.

File details

Details for the file accera_gpu-1.2.13-cp37-cp37m-win_amd64.whl.

File metadata

File hashes

Hashes for accera_gpu-1.2.13-cp37-cp37m-win_amd64.whl
Algorithm Hash digest
SHA256 bb14a27d80783e42844980d9c4b6799787027bb69cf6b9ce2160aba6df3a6082
MD5 baa16d77412d75e43c97f23607369bd4
BLAKE2b-256 d3b4493c567a72ac94ff6f5799088573eca65ad0a73c2b31251ce65a3bfd4c67

See more details on using hashes here.

File details

Details for the file accera_gpu-1.2.13-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for accera_gpu-1.2.13-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 3ea3de6c093c5ba772e6b1257de20f4f7f672314d1b1fb711a9083e6aff6e30f
MD5 5eeac45d50628f540dade6cee3441dac
BLAKE2b-256 f55a6d1cb5505c423955003ac58ac76561c90541323665b2544248826f472bef

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