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MCBooster: a library for fast Monte Carlo generation of phase-space decays on massively parallel platforms

A. A. Alves Jr, M. D. Sokoloff

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Tags: Computational Physics, CUDA, High Energy Physics - Experiment, High Energy Physics - Phenomenology, Intel Xeon Phi, Monte Carlo simulation, nVidia, nVidia GeForce GTX 970, nVidia GeForce GTX Titan Z, nVidia Quadro K 2200, OpenMP, Package, Physics, Tesla K40

February 22, 2017 by hgpu

Best Practice Guide Intel Xeon Phi v2.0

Emanouil Atanassov, Michaela Barth, Mikko Byckling, Vali Codreanu, Nevena Ilieva, Tomas Karasek, Jorge Rodriguez, Sami Saarinen, Ole Widar Saastad, Michael Schliephake, Martin Stachon, Janko Strassburg, Volker Weinberg (Editor)

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Tags: Benchmarking, Computer science, Intel Xeon Phi, OpenMP, Performance

February 14, 2017 by hgpu

Clustering Throughput Optimization on the GPU

Michael Gowanlock, Cody M. Rude, David M. Blair, Justin D. Li, Victor Pankratius

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Tags: Algorithms, Clustering, Computer science, CUDA, Databases, nVidia, OpenMP, Tesla K20

February 5, 2017 by hgpu

GraviDy: a GPU modular, parallel N-body integrator

Cristian Maureira-Fredes, Pau Amaro-Seoane

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Tags: Astrophysics, CUDA, Globular clusters, Gravitation, Instrumentation and Methods for Astrophysics, MPI, nVidia, OpenMP, Package, Physics, Tesla C2050, Tesla C2070, Tesla M2050

February 5, 2017 by hgpu

Parallel Implementations of the Cholesky Decomposition on CPUs and GPUs

Joao Paulo Tarasconi Ruschel

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Tags: Algorithms, Benchmarking, Computer science, CUDA, Linear Algebra, Matrix decomposition, nVidia, OpenCL, OpenMP, Package, Performance, Tesla K80, Thesis

January 26, 2017 by hgpu

Deep Convolutional Network evaluation on the Intel Xeon Phi: Where Subword Parallelism meets Many-Core

Gaurav Raina

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Tags: Computer science, Computer vision, Deep learning, Intel Xeon Phi, Neural networks, OpenMP, Thesis

January 26, 2017 by hgpu

Android Malware Classification Using Parallelized Machine Learning Methods

Lifan Xu

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Tags: ATI, ATI Radeon HD 7970, Computer science, Deep learning, Machine learning, Neural networks, nVidia, OpenCL, OpenMP, Security, Tesla C2050, Thesis

December 31, 2016 by hgpu

Performance Optimisation of Smoothed Particle Hydrodynamics Algorithms for Multi/Many-Core Architectures

Fabio Baruffa, Luigi Iapichino, Nicolay J. Hammer, Vasileios Karakasis

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Tags: Algorithms, Astrophysics, Computational Physics, Computer science, Instrumentation and Methods for Astrophysics, Intel Xeon Phi, OpenMP, Performance, Physics

December 20, 2016 by hgpu

Toward Automatic Translation: From OpenACC to OpenMP 4

Nawrin Sultana

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Tags: Algorithms, Code generation, Compilers, Computer science, nVidia, OpenACC, OpenMP, Tesla K40, Thesis

December 17, 2016 by hgpu

Translating OpenMP Device Constructs to OpenCL using Unnecessary Data Transfer Elimination

Junghyun Kim, Yong-Jun Lee, Jungho Park, Jaejin Lee

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Tags: AMD Radeon R9 390X, ATI, Benchmarking, Code generation, Computer science, nVidia, nVidia GeForce GTX Titan X, OpenACC, OpenCL, OpenMP

December 14, 2016 by hgpu

Towards Comprehensive Parametric Code Generation Targeting Graphics Processing Units in Support of Scientific Computation

Ning Xie

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Tags: Code generation, Computer science, CUDA, nVidia, OpenMP, Tesla M2050, Thesis

December 14, 2016 by hgpu

Performance Evaluation and Optimization of HPCG benchmark on CPU + MIC platform

Qingyi Pan, Xiaoying Wang

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Tags: Benchmarking, Computer science, Heterogeneous systems, Intel Xeon Phi, OpenMP, Performance

December 10, 2016 by hgpu

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

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

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high performance computing on graphics processing units: hgpu.org

MCBooster: a library for fast Monte Carlo generation of phase-space decays on massively parallel platforms

Best Practice Guide Intel Xeon Phi v2.0

Clustering Throughput Optimization on the GPU

GraviDy: a GPU modular, parallel N-body integrator

Parallel Implementations of the Cholesky Decomposition on CPUs and GPUs

Deep Convolutional Network evaluation on the Intel Xeon Phi: Where Subword Parallelism meets Many-Core

Android Malware Classification Using Parallelized Machine Learning Methods

Performance Optimisation of Smoothed Particle Hydrodynamics Algorithms for Multi/Many-Core Architectures

Toward Automatic Translation: From OpenACC to OpenMP 4

Translating OpenMP Device Constructs to OpenCL using Unnecessary Data Transfer Elimination

Towards Comprehensive Parametric Code Generation Targeting Graphics Processing Units in Support of Scientific Computation

Performance Evaluation and Optimization of HPCG benchmark on CPU + MIC platform

Recent source codes

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MSCCL++: A GPU-driven communication stack for scalable AI applications

Benchmark compute shader of Unity against InteropUnityCUDA

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

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

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