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opencl
Here are 877 public repositories matching this topic...
Open deep learning compiler stack for cpu, gpu and specialized accelerators
javascript
machine-learning
performance
deep-learning
metal
compiler
gpu
vulkan
opencl
tensor
spirv
rocm
tvm
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Sep 6, 2020 - Python
MACE is a deep learning inference framework optimized for mobile heterogeneous computing platforms.
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Aug 7, 2020 - C++
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umar456
commented
Jan 21, 2019
Current implementation of join can be improved by performing the operation in a single call to the backend kernel instead of multiple calls.
This is a fairly easy kernel and may be a good issue for someone getting to know CUDA/ArrayFire internals. Ping me if you want additional info.
Spasi
commented
Jan 25, 2018
As of 00e1f52cf95c16b0cea34c83372145532b4745ba the LWJGL codebase has a new directory layout. This has broken the included Eclipse project.
Open
Assimp Tutorial
7
The ARM Computer Vision and Machine Learning library is a set of functions optimised for both ARM CPUs and GPUs using SIMD technologies.
android
machine-learning
arm
computer-vision
neural-network
cpp
neon
opencl
simd
armv7
aarch64
armv8
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Aug 28, 2020 - C++
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Sep 11, 2018 - C++
athas
commented
Jun 8, 2020
I often use -v just to see that something is going on, but a progress bar (enabled by default) would serve the same purpose and be more concise.
We can just factor out the code from futhark bench for this.
A C++ GPU Computing Library for OpenCL
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Aug 14, 2020 - C++
Fast Clojure Matrix Library
java
api
clojure
gpu
opencl
matrix
cuda
matrix-factorization
clojure-library
matrix-functions
gpgpu
matrix-multiplication
high-performance-computing
gpu-computing
vectorization
matrix-calculations
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Sep 2, 2020 - Clojure
A fast, ergonomic and portable tensor library in Nim with a deep learning focus for CPU, GPU and embedded devices via OpenMP, Cuda and OpenCL backends
iot
machine-learning
nim
deep-learning
opencl
linear-algebra
automatic-differentiation
openmp
parallel-computing
cuda
autograd
gpgpu
neural-networks
high-performance-computing
ndarray
tensor
gpu-computing
multidimensional-arrays
cudnn
matrix-library
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Aug 11, 2020 - Nim
OpenCL integration for Python, plus shiny features
python
sorting
performance
opengl
amd
gpu
opencl
array
parallel-computing
cuda
reduction
nvidia
scientific-computing
prefix-sum
heterogeneous-parallel-programming
parallel-algorithm
shared-memory
multidimensional-arrays
pyopencl
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Sep 1, 2020 - Python
An OpenCL-based FPGA Accelerator for Convolutional Neural Networks
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Jul 28, 2020 - C
VexCL is a C++ vector expression template library for OpenCL/CUDA/OpenMP
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Jul 10, 2020 - C++
OpenCL implementation running on the VideoCore IV GPU of the Raspberry Pi models
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Sep 2, 2020 - C++
Tuned OpenCL BLAS
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Aug 16, 2020 - C++
LuxCore source repository
visualization
opencl
ray-tracer
cuda
raytracer
raytracing
gpu-computing
ray
ray-tracing
3d-graphics
rtx
optix
pathtracer
path-tracing
bidirectional-path-tracing
luxrender
luxcorerender
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Sep 5, 2020 - C++
Connectionist Temporal Classification (CTC) decoding algorithms: best path, prefix search, beam search and token passing. Implemented in Python and OpenCL.
python
opencl
recurrent-neural-networks
speech-recognition
beam-search
family
language-model
handwriting-recognition
ctc
loss
prefix-search
ctc-loss
fak-friend
level-lm
token-passing
best-path
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Jan 9, 2020 - Python
jjfumero
commented
May 5, 2020
The TornadoVM repository contains a module with several examples. Include more use cases for acceleration with TornadoVM, such as a graphical version for NBODY, filter for photography, block-chain. etc.
BitCracker is the first open source password cracking tool for memory units encrypted with BitLocker
microsoft
windows
cryptography
attack
gpu
opencl
cuda
hash
gpgpu
passwords
cracking
john-the-ripper
bitlocker
password-cracker
decryption-algorithm
bitcracker
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Jan 28, 2019 - C
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Our users are often confused by the output from programs such as zip2john sometimes being very large (multi-gigabyte). Maybe we should identify and enhance these programs to output a message to stderr to explain to users that it's normal for the output to be very large - maybe always or maybe only when the output size is above a threshold (e.g., 1 million bytes?)