KO
|
EN
gitlite — search
Search
#typescript
#ai-agents
#ai
#dsh-plugin
#deepseek-harness
#open-source
#cli
#claude-code
#codex
#developer-tools
#react
#windows
ff-gpu
★ 15
Open GitHub ↗
Finite Field Operations on GPGPU
Download README (.md)
Explore Similar Repositories
tiny-dpcpp-nn
:
SYCL implementation of Fused MLPs for Intel GPUs
oneAPI_course
:
oneAPI - Data Parallel C++ course for students
blake3
:
SYCL accelerated BLAKE3 Hash Implementation
dpcpp-tutorial
:
Lectures and Labs for Data Parallel Computing and DPC++. Sponsored by Intel Corporation.
ff-GPGPU
:
Finite Field based on GPU
// repository documentation
Was this content helpful?
★ 0
(0 ratings)
Select Rating:
★
★
★
★
★
Submit Feedback
Recent Feedback
×
Download README
Do you want to download the
README.md
file for
ff-gpu
?
Download (.md)
# ff-gpu Finite Field Operations on GPGPU ## Background In recent times, I've been interested in Finite Field operations, so I decided to implement few fields in SYCL DPC++, targeting accelerators ( specifically GPGPUs ). In this repository, currently I keep implementation of two finite field's arithmetic operations, accompanied with relevant benchmarks on both CPU, GPGPU. - Binary Extension Field `F(2 ** 32)` - Prime Field `F(2 ** 64 - 2 ** 32 + 1)` I've also written following implementations, along with benchmark results on CPU, GPU. - Rescue Prime Hash function - Merkle Tree Construction using Rescue Prime Hash - Six step algorithm based (I)*N*umber *T*heoretic *T*ransform - Cooley-Tukey algorithm based (I)NTT - DFT-style (I)NTT ## Prerequisites - Make sure you've `make`, `clang-format` and `dpcpp`/ `clang++` installed - I suggest you build llvm-based DPC++ compiler from source, check [here](https://intel.github.io/llvm-docs/GetStartedGuide.html#prerequisites) [ **required for CUDA backend** ] - Or you may want to download pre-compiled Intel oneAPI toolkit, includes both compilers, check [here](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit-download.html) ## Benchmarks - I'm running ```bash $ lsb_release -d Description: Ubuntu 20.04.3 LTS ``` - As compiler, I'm using ```bash $ dpcpp --version Intel(R) oneAPI DPC++/C++ Compiler 2022.0.0 (2022.0.0.20211123) Target: x86_64-unknown-linux-gnu Thread model: posix ``` or ```bash $ clang++ --version clang version 14.0.0 (https://github.com/intel/llvm dc9bd3fafdeacd28528eb4b1fef3ad9b76ef3b92) Target: x86_64-unknown-linux-gnu Thread model: posix ``` - Compile, link & run ```bash make # JIT kernel compilation on *default* device, for AOT read below ./run ``` - But you may be interested in targeting specific device, if you've multiple devices to choose from in runtime. Specify target device (which is to be used in runtime) during compilation phase using ```bash DEVICE=cpu make # still JIT, but in runtime use CPU DEVICE=gpu make # still JIT, but in runtime use GPU DEVICE=host make # still JIT, but in runtime use HOST ``` - Clean using ```bash make clean ``` - Format source, if required ```bash make format ``` - Aforementioned steps compile kernels JIT, but if target device is already known it's better to compile them AOT, which saves some time in runtime, though compiled binary is device specific now. - I provide AOT kernel compilation recipe for CPUs using `avx2` instructions. You can check whether your CPU supports that. ```bash lscpu | grep -i avx ``` - AOT Compilation targeting CPU can be invoked using ```bash DEVICE=cpu make aot_cpu ``` - I also provide recipe for AOT compiling kernels targeting Intel Iris Xe Max Graphics ```bash DEVICE=gpu make aot_gpu ``` > You may have some other hardware, consider taking a look at AOT compilation [guidelines](https://www.intel.com/content/www/us/en/develop/documentation/oneapi-dpcpp-cpp-compiler-dev-guide-and-reference/top/compilation/ahead-of-time-compilation.html) & make necessary changes in `Makefile`. --- **Targeting Nvidia GPU with CUDA backend :** > For targeting Nvidia GPU, you want to run `DEVICE=gpu make cuda`, so that benchmark suite is compiled for CUDA backend. I suggest you read [this](https://github.com/itzmeanjan/ff-p254-gpu/blob/acbedab1dd653f7270ac917755286c565d2a8927/README.md#prerequisite) for setting up your machine with Nvidia GPU, if you've not yet. --- I run benchmark suite on both **Intel CPU/ GPU** and **Nvidia GPU**, keeping results 👇 - Intel CPU/ GPU - [Rescue Prime Hash on `F(2 ** 64 - 2 ** 32 + 1)`](./benchmarks/rescue_prime.md) - [Merkle Tree Construction Using Rescue Prime Hash](./benchmarks/merkle_tree.md) - [(Inverse) Number Theoretic Transform on `F(2 ** 64 - 2 ** 32 + 1)`](benchmarks/ntt.md) - DFT style (I)NTT - Cooley-Tukey (I)FFT - Six Step Algorithm (I)FFT - Nvidia GPU - [Rescue Prime Hash on `F(2 ** 64 - 2 ** 32 + 1)`](benchmarks/cuda_rescue_prime.md) - [Merkel Tree Construction](benchmarks/cuda_merkle_tree.md) - [(Inverse) Number Theoretic Transform on `F(2 ** 64 - 2 ** 32 + 1)`](benchmarks/cuda_ntt.md) - Cooley-Tukey (I)FFT - Six Step Algorithm (I)FFT ## Tests You can run basic test cases using ```bash # set variable to runtime target device DEVICE=cpu|gpu|host make test ``` There's another set of randomised test cases, which asserts results *( obtained from my prime field implementation )* with another finite field implementation module, written in `Python`, named `galois`. For running those, I suggest you first compile shared object using ```bash # set variable to runtime target device DEVICE=cpu|gpu|host make genlib ``` After that you can follow next steps [here](wrapper/python).
