AdaptiveCpp
NumPy
AdaptiveCpp | NumPy | |
---|---|---|
19 | 272 | |
1,046 | 26,459 | |
2.8% | 1.2% | |
9.7 | 10.0 | |
1 day ago | 1 day ago | |
C++ | Python | |
BSD 2-clause "Simplified" License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
AdaptiveCpp
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What Every Developer Should Know About GPU Computing
Sapphire Rapids is a CPU.
AMD's primary focus for a GPU software ecosystem these days seems to be implementing CUDA with s/cuda/hip, so AMD directly supports and encourages running GPU software written in CUDA on AMD GPUs.
The only implementation for sycl on AMD GPUs that I can find is a hobby project that apparently is not allowed to use either the 'hip' or 'sycl' names. https://github.com/AdaptiveCpp/AdaptiveCpp
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AMD May Get Across the CUDA Moat
Not natively, but AdaptiveCpp (previously hiSycl, then OpenSycl) has a single source single compiler pass, where they basically store LLVM IR as an intermediate representation.
https://github.com/AdaptiveCpp/AdaptiveCpp/blob/develop/doc/...
Performance penalty was within ew precents, at least according to the paper (figure 9 and 10)
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Offloading standard C++ PSTL to Intel, NVIDIA and AMD GPUs with AdaptiveCpp
AdaptiveCpp (formerly known as hipSYCL) is an independent, open source, clang-based heterogeneous C++ compiler project. I thought some of you might be interested in knowing that we recently added support to offload standard C++ parallel STL algorithms to GPUs from all major vendors. E.g.:
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AMD's HIPRT Working Its Way To Blender With ~25% Faster Rendering
In fact SYCL was initially called hipSYCL because it is based on AMD's ROCm/HIP. AMD had hipSYCL code running on the Frontier supercomputer four years ago at least and continues to support it.
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hipSYCL can now generate a binary that runs on any Intel/NVIDIA/AMD GPU - in a single compiler pass. It is now the first single-pass SYCL compiler, and the first with unified code representation across backends.
Apple Silicon support through Metal is something that is actively discussed in hipSYCL. See https://github.com/illuhad/hipSYCL/issues/864 https://github.com/illuhad/hipSYCL/issues/460 (loooong discussion)
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Bringing Nvidia® and AMD support to oneAPI
But really, the DPC++ part of oneAPI (which is many APIs) is just SYCL + extensions, and there are several other SYCL implementations which have already featured CUDA and Hip (AMD) support for a long time. The most popular and widely-used is hipSYCL, which we've been using in an HPC context on NV hardware for over 4 years now.
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Intel oneAPI 2023 Released - AMD & NVIDIA Plugins Available
Unfortunately, the AMD and Nvidia plugins are proprietary. AMD users are probably better served with hipSYCL, if they somehow find an application using SYCL...
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There is framework for everything.
Also, you might want to take a look at an implementation like hipSYCL :)
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The Next Platform: "Intel Takes The SYCL To Nvidia's CUDA With Migration Tool"
Yup. SYCL is the future: https://github.com/illuhad/hipSYCL
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Phoronix: "Intel's Vulkan Linux Driver Adds Experimental Mesh Shader Support For DG2/Alchemist"
ROCm is completely independent from these. It's a compute stack containing an OpenCL implementation for Radeon GPUs, plus a CUDA-like language called HIP which can be compiled to either device code for Radeon GPUs or to PTX to work with Nvidia GPUs. However, some researchers also created hipSYCL that allows SYCL to run atop HIP; you can think of it like DXVK - the program contains the DirectX/SYCL API, and DXVK/hipSYCL converts it to Vulkan/HIP (with one difference - DXVK does the conversion at runtime, while hipSYCL does it at compile time).
NumPy
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Dot vs Matrix vs Element-wise multiplication in PyTorch
In NumPy with @, dot() or matmul():
- NumPy 2.0.0 Beta1
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Element-wise vs Matrix vs Dot multiplication
In NumPy with * or multiply(). ` or multiply()` can multiply 0D or more D arrays by element-wise multiplication.
- JSON dans les projets data science : Trucs & Astuces
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JSON in data science projects: tips & tricks
Data science projects often use numpy. However, numpy objects are not JSON-serializable and therefore require conversion to standard python objects in order to be saved:
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Introducing Flama for Robust Machine Learning APIs
numpy: A library for scientific computing in Python
- help with installing numpy, please
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A Comprehensive Guide to NumPy Arrays
Python has become a preferred language for data analysis due to its simplicity and robust library ecosystem. Among these, NumPy stands out with its efficient handling of numerical data. Let’s say you’re working with numbers for large data sets—something Python’s native data structures may find challenging. That’s where NumPy arrays come into play, making numerical computations seamless and speedy.
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Why do all the popular projects use relative imports in __init__ files if PEP 8 recommends absolute?
I was looking at all the big projects like numpy, pytorch, flask, etc.
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NumPy 2.0 development status & announcements: major C-API and Python API cleanup
I wish the NumPy devs would more thoroughly consider adding full fluent API support, e.g. x.sqrt().ceil(). [Issue #24081]
What are some alternatives?
ROCm - AMD ROCm™ Software - GitHub Home [Moved to: https://github.com/ROCm/ROCm]
SymPy - A computer algebra system written in pure Python
HIP-CPU - An implementation of HIP that works on CPUs, across OSes.
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
triSYCL - Generic system-wide modern C++ for heterogeneous platforms with SYCL from Khronos Group
blaze - NumPy and Pandas interface to Big Data
HIP - HIP: C++ Heterogeneous-Compute Interface for Portability
SciPy - SciPy library main repository
cuda-api-wrappers - Thin C++-flavored header-only wrappers for core CUDA APIs: Runtime, Driver, NVRTC, NVTX.
Numba - NumPy aware dynamic Python compiler using LLVM
cuda_memtest - Fork of CUDA GPU memtest :eyeglasses:
Nim - Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula. Its design focuses on efficiency, expressiveness, and elegance (in that order of priority).