high performance computing on graphics processing units: hgpu.org

Ashwin M. Aji, Lokendra S. Panwar, Feng Ji, Milind Chabbi, Karthik Murthy, Pavan Balaji, Keith R. Bisset, James Dinan, Wu-chun Feng, John Mellor-Crummey, Xiaosong May, Rajeev Thakur

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Tags: Computer science, CUDA, GPU cluster, Heterogeneous systems, MPI, nVidia, Optimization, Seismology, Tesla M2050

April 22, 2013 by hgpu

Graphics processing unit (GPU) programming strategies and trends in GPU computing

Andre R. Brodtkorb, Trond R. Hagen, Martin L. Saetra

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Tags: Computer science, Heterogeneous systems, Metaheuristics, Optimization, Overview, Programming techniques

April 19, 2013 by hgpu

GPU Computing in Discrete Optimization – Part II: Survey Focused on Routing Problems

Christian Schulz, Geir Hasle, Andre R. Brodtkorb, Trond R. Hagen

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, Optimization, Tutorial

April 19, 2013 by hgpu

GPU Computing in Discrete Optimization – Part I: Introduction to the GPU

Andre R. Brodtkorb, Trond R. Hagen, Christian Schulz, Geir Hasle

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, Optimization, Tutorial

April 19, 2013 by hgpu

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

MSCCL++: Rethinking GPU Communication Abstractions for Cutting-edge AI Applications

Benchmark compute shader of Unity against InteropUnityCUDA

InteropUnityCUDA: A Tool for Interoperability Between Unity and CUDA

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

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high performance computing on graphics processing units: hgpu.org

Tiling optimizations for stencil computations

Parallelization of the Ant Colony Optimization for the Shortest Path Problem using OpenMP and CUDA

Solving the Coalition Structure Generation Problem on a GPU

Parallel Implementations of a Disparity Estimation Algorithm Based on a Proximal Splitting Method

Real-time Stochastic Optimization of Complex Energy Systems on High Performance Computers

Optimizing Similarity Computations for Ontology Matching – Experiences from GOMMA

Multigrid Optimization Methods for High Performance Computing

Modeling and Optimization of Parallel Matrix-based Computations on GPU

On the Efficacy of GPU-Integrated MPI for Scientific Applications

Graphics processing unit (GPU) programming strategies and trends in GPU computing

GPU Computing in Discrete Optimization – Part II: Survey Focused on Routing Problems

GPU Computing in Discrete Optimization – Part I: Introduction to the GPU

Recent source codes

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

Shamrock: Multi-GPU hydrodynamics for astrophysics

LLMPerf: GPU Performance Modeling meets Large Language Models

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