high performance computing on graphics processing units: hgpu.org

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Compiling and Optimizing OpenMP 4.X Programs to OpenCL and SPIR

Marcio M. Pereira, Rafael C. F. Sousa, Guido Araujo

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Tags: ARM, Code generation, Compilers, Computer science, FPGA, Heterogeneous systems, LLVM, nVidia, OpenCL, OpenMP, Package, Tesla K40

November 21, 2017 by hgpu

Hydra: a C++11 framework for data analysis in massively parallel platforms

A. A. Alves Junior, M. D. Sokoloff

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Tags: Computational Physics, Computer science, CUDA, Mathematical Software, nVidia, OpenMP, Package, Tesla K40

November 16, 2017 by hgpu

Domain-Specific Acceleration and Auto-Parallelization of Legacy Scientific Code in FORTRAN 77 using Source-to-Source Compilation

Wim Vanderbauwhede, Gavin Davidson

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Tags: Compilers, Computer science, Fortran, Heterogeneous systems, Mathematical Software, Numerical Analysis, nVidia, nVidia GeForce GTX Titan X, OpenACC, OpenCL, OpenMP, Package

November 16, 2017 by hgpu

Accelerating HPC codes on Intel(R) Omni-Path Architecture networks: From particle physics to Machine Learning

Peter Boyle, Michael Chuvelev, Guido Cossu, Christopher Kelly, Christoph Lehner, Lawrence Meadows

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Tags: Benchmarking, Computer science, Intel Xeon Phi, Machine learning, MPI, OpenMP, Package, Performance, Physics

November 16, 2017 by hgpu

Scalable and massively parallel Monte Carlo photon transport simulations for heterogeneous computing platforms

Leiming Yu, Fanny Nina-Paravecino, David Kaeli, Qianqian Fang

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Tags: Algorithms, AMD R9 Nano, AMD Radeon RX 480, ATI, Computational Physics, Heterogeneous systems, nVidia, nVidia GeForce GTX 1050 Ti, nVidia GeForce GTX 1080 Ti, nVidia GeForce GTX 590, nVidia GeForce GTX 980 Ti, nVidia GeForce GTX Titan X, OpenCL, Package, Physics

November 12, 2017 by hgpu

Performance Evaluation of Deep Learning Tools in Docker Containers

Pengfei Xu, Shaohuai Shi, Xiaowen Chu

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Tags: Benchmarking, Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX 980, nVidia GeForce GTX Titan X, Package, Performance

November 12, 2017 by hgpu

ChainerMN: Scalable Distributed Deep Learning Framework

Takuya Akiba, Keisuke Fukuda, Shuji Suzuki

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Tags: Computer science, CUDA, Deep learning, nVidia, nVidia GeForce GTX Titan X, Package, Python

November 5, 2017 by hgpu

GooFit 2.0

Henry Schreiner, Christoph Hasse, Bradley Hittle, Himadri Pandey, Michael Sokoloff, Karen Tomko

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Tags: Computer science, CUDA, Mathematical Software, MPI, nVidia, nVidia GeForce GTX 1050 Ti, nVidia GeForce GTX 1080 Ti, Package, Probability, Tesla K40, Tesla P100

October 29, 2017 by hgpu

Hybrid Fortran: High Productivity GPU Porting Framework Applied to Japanese Weather Prediction Model

Michel Muller, Takayuki Aoki

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Tags: Earth and Space Sciences, Fortran, nVidia, OpenACC, OpenMP, Package, Tesla K20, Tesla P100, Weather prediction, Weather Prediction Model

October 29, 2017 by hgpu

Parallel Computing for the Inverse of SPD matrix

Yan Shen, Yuxing Dai, Zhiliang Zhu

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Tags: AMD Radeon R9 290, ATI, Computer science, Factorization, Linear Algebra, OpenCL, Package

October 24, 2017 by hgpu

Wilson and Domainwall Kernels on Oakforest-PACS

Issaku Kanamori, Hideo Matsufuru

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Tags: HEP, High Energy Physics - Lattice, Intel Xeon Phi, Mathematical Software, MPI, OpenMP, Package, Physics, QCD

October 21, 2017 by hgpu

Synkhronos: a Multi-GPU Theano Extension for Data Parallelism

Adam Stooke, Pieter Abbeel

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Tags: Computer science, CUDA, Data parallelism, Deep learning, nVidia, nVidia DGX-1, Package, Tesla P100, Theano

October 15, 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

Compiling and Optimizing OpenMP 4.X Programs to OpenCL and SPIR

Hydra: a C++11 framework for data analysis in massively parallel platforms

Domain-Specific Acceleration and Auto-Parallelization of Legacy Scientific Code in FORTRAN 77 using Source-to-Source Compilation

Accelerating HPC codes on Intel(R) Omni-Path Architecture networks: From particle physics to Machine Learning

Scalable and massively parallel Monte Carlo photon transport simulations for heterogeneous computing platforms

Performance Evaluation of Deep Learning Tools in Docker Containers

ChainerMN: Scalable Distributed Deep Learning Framework

GooFit 2.0

Hybrid Fortran: High Productivity GPU Porting Framework Applied to Japanese Weather Prediction Model

Parallel Computing for the Inverse of SPD matrix

Wilson and Domainwall Kernels on Oakforest-PACS

Synkhronos: a Multi-GPU Theano Extension for Data Parallelism

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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