high performance computing on graphics processing units: hgpu.org

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GPU Pro 7: Advanced Rendering

Wolfgang Engel (editor)

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Tags: 3D Graphics and Realism, Book, Computer science, CUDA, DirectCompute, DirectX, nVidia, OpenGL, Package, Rendering, Visualization

November 26, 2017 by hgpu

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

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

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

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

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

GigaAPI for GPU Parallelization

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

Advances in Semantic Patching for HPC-oriented Refactorings with Coccinelle

DuoReduce: MLIR's benchmark

Hardware-Assisted Software Testing and Debugging for Heterogeneous Computing

Shamrock: Multi-GPU hydrodynamics for astrophysics

The Shamrock code: I- Smoothed Particle Hydrodynamics on GPUs

LLMPerf: GPU Performance Modeling meets Large Language Models

Hercules: A Compiler for Productive Programming of Heterogeneous Systems

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high performance computing on graphics processing units: hgpu.org

Packages

GPU Pro 7: Advanced Rendering

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

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

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

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

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

Recent source codes

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

GigaAPI: a user-space API that simplifies multi-GPU programming, bridging the gap between the capabilities of parallel GPU systems and the ability of developers to harness their full potential

Coccinelle: a C code transformation engine using SmPL for matches, refactorings, and bug fixing

DuoReduce: MLIR's benchmark

Shamrock: Multi-GPU hydrodynamics for astrophysics

LLMPerf: GPU Performance Modeling meets Large Language Models

Hercules: A Compiler for Productive Programming of Heterogeneous Systems

Celerity Runtime: High-level C++ for Accelerator Clusters

wgpy: WebGL accelerated numpy-compatible array library for web browser

Microbenchmarking OpenMP target offload with Catch2

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