high performance computing on graphics processing units: hgpu.org

Packages

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Improving Communication Performance in GPU-Accelerated HPC Clusters

Iman Faraji

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Tags: Computer science, CUDA, GPU cluster, MPI, nVidia, Package, Performance, Thesis

January 28, 2018 by hgpu

Comprehensive Optimization of Parametric Kernels for Graphics Processing Units

Xiaohui Chen, Marc Moreno-Maza, Jeeva Paudel, Ning Xie

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Tags: Code generation, Computer science, CUDA, LLVM, nVidia, Package, Performance, PTX, Symbolic Computation, Tesla M2050

January 20, 2018 by hgpu

ImageCL: Language and source-to-source compiler for performance portability, load balancing, and scalability prediction on heterogeneous systems

Thomas L. Falch, Anne C. Elster

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Tags: ATI, ATI Radeon HD 7970, Compilers, Heterogeneous systems, Image processing, Machine learning, nVidia, nVidia GeForce GTX 960, OpenCL, Package, performance portability, Tesla K40

January 13, 2018 by hgpu

pyPaSWAS: Python-based multi-core CPU and GPU sequence alignment

Sven Warris, N. Roshan N. Timal, Marcel Kempenaar, Arne M. Poortinga, Henri van de Geest, Ana L. Varbanescu, Jan-Peter Nap

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Tags: Bioinformatics, Biology, CUDA, nVidia, nVidia GeForce GTX 1070, OpenCL, Package, Python, Sequence alignment, Smith-Waterman algorithm

January 13, 2018 by hgpu

High Performance Stencil Code Generation with Lift

Bastian Hagedorn, Larisa Stoltzfus, Michel Steuwer, Sergei Gorlatch, Christophe Dubach

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Tags: ARM, ATI, ATI Radeon HD 7970, Code generation, Compilers, Computer science, Linear Algebra, nVidia, OpenCL, Package, performance portability, Scala, Tesla K20

January 13, 2018 by hgpu

ThunderSVM: A Fast SVM Library on GPUs and CPUs

Zeyi Wen, Jiashuai Shi, Bingsheng He, Qinbin Li, Jian Chen

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Tags: Computer science, CUDA, Data mining, Machine learning, nVidia, OpenMP, Package, Tesla P100

January 6, 2018 by hgpu

Rubus: A compiler for seamless and extensible parallelism

Muhammad Adnan, Faisal Aslam, Zubair Nawaz, Syed Mansoor Sarwar

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Tags: Aparapi, Benchmarking, Computer science, CUDA, Java, Matrix multiplication, nVidia, nVidia GeForce GT 630 M, OpenCL, OpenGL, Package

January 6, 2018 by hgpu

Design and Implementation of the Futhark Programming Language

Troels Henriksen

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Tags: AMD FirePro W8100, ATI, Computer science, Haskell, High-level Languages, nVidia, OpenCL, Package, Tesla K40, Thesis

December 28, 2017 by hgpu

A Generic Inverted Index Framework for Similarity Search on the GPU

Jingbo Zhou, Qi Guo, H. V. Jagadish, Lubos Krcal, Siyuan Liu, Wenhao Luan, Anthony K. H. Tung, Yueji Yang, Yuxin Zheng

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Tags: Computer science, CUDA, Hashing, Machine learning, Nearest neighbour, nVidia, nVidia GeForce GTX Titan X, OpenMPI, Package

December 28, 2017 by hgpu

Pass a Pointer: Exploring Shared Virtual Memory Abstractions in OpenCL Tools for FPGAs

Felix Winterstein, George Constantinides

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Tags: Computer science, FPGA, Heterogeneous systems, OpenCL, Package

December 24, 2017 by hgpu

An MPI-Based Python Framework for Distributed Training with Keras

Dustin Anderson, Jean-Roch Vlimant, Maria Spiropulu

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Tags: Computer science, CUDA, Deep learning, Keras, Machine learning, MPI, Neural networks, nVidia, nVidia GeForce GTX 1080, Package, Python, Tesla K80

December 24, 2017 by hgpu

GAMER-2: a GPU-accelerated adaptive mesh refinement code — accuracy, performance, and scalability

Hsi-Yu Schive, John A. ZuHone, Nathan J. Goldbaum, Matthew J. Turk, Massimo Gaspari, Chin-Yu Cheng

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Tags: ARM, Astrophysics, Chemistry, CUDA, Instrumentation and Methods for Astrophysics, Magnetohydrodynamics, MPI, nVidia, OpenMP, Package, Tesla K20, Tesla P100

December 24, 2017 by hgpu

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

DVM: Real-Time Kernel Generation for Dynamic AI Models

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

AutoKernel: Autonomous GPU Kernel Optimization via Iterative Agent-Driven Search

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

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high performance computing on graphics processing units: hgpu.org

Packages

Improving Communication Performance in GPU-Accelerated HPC Clusters

Comprehensive Optimization of Parametric Kernels for Graphics Processing Units

ImageCL: Language and source-to-source compiler for performance portability, load balancing, and scalability prediction on heterogeneous systems

pyPaSWAS: Python-based multi-core CPU and GPU sequence alignment

High Performance Stencil Code Generation with Lift

ThunderSVM: A Fast SVM Library on GPUs and CPUs

Rubus: A compiler for seamless and extensible parallelism

Design and Implementation of the Futhark Programming Language

A Generic Inverted Index Framework for Similarity Search on the GPU

Pass a Pointer: Exploring Shared Virtual Memory Abstractions in OpenCL Tools for FPGAs

An MPI-Based Python Framework for Distributed Training with Keras

GAMER-2: a GPU-accelerated adaptive mesh refinement code — accuracy, performance, and scalability

Recent source codes

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

LLM.Q: Quantized LLM training in pure CUDA/C++

True 4-Bit Quantized CNN Training on CPU

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

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