high performance computing on graphics processing units: hgpu.org

hgpu.org » Heterogeneous systems

On the Efficacy of GPU-Integrated MPI for Scientific Applications

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

Programming Models and Runtimes for Heterogeneous Systems

Max Grossman

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Tags: Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 480, Tesla C2050, Tesla M2050, Thesis

April 22, 2013 by hgpu

Valar: A Benchmark Suite to Study the Dynamic Behavior of Heterogeneous Systems

Perhaad Mistry, Yash Ukidave, Dana Schaa, David Kaeli

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Tags: APU, ATI, ATI Radeon HD 7850, ATI Radeon HD 7970, Benchmarking, Computer science, Computer vision, Heterogeneous systems, OpenCL, Performance

April 22, 2013 by hgpu

An Automatic Input-Sensitive Approach for Heterogeneous Task Partitioning

Klaus Kofler, Ivan Grasso, Biagio Cosenza, Thomas Fahringer

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Tags: ATI, ATI Radeon HD 5870, Code generation, Compilers, Computer science, Heterogeneous systems, Machine learning, Neural networks, nVidia, nVidia GeForce GTX 480, OpenCL, Performance, Task scheduling

April 21, 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

Supporting Iteration in a Heterogeneous Data Flow Engine

Jon Currey, Simon Baker, Christopher J. Rossbach

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Tags: Algorithms, Computer science, Computer vision, DirectCompute, Heterogeneous systems, MapReduce, nVidia, nVidia GeForce GTX 580, Optical flow

April 18, 2013 by hgpu

A Framework for Profiling and Performance Monitoring of Heterogeneous Applications

Perhaad Mistry, Yash Ukidave, Dana Schaa, David Kaeli

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Tags: ATI, ATI Radeon HD 5870, ATI Radeon HD 6550, Computer science, Computer vision, Heterogeneous systems, OpenCL, Performance, Signal processing

April 17, 2013 by hgpu

Co-processing SPMD Computation on GPUs and CPUs on Shared Memory System

Hui Li, Geoffrey Fox, Gregor Laszewski, Zhenhua Guo, Judy Qiu

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Tags: Clustering, CUDA, Heterogeneous systems, MapReduce, Matrix multiplication, nVidia

April 13, 2013 by hgpu

High Performance FFT Based Poisson Solver on a CPU-GPU Heterogeneous Platform

Jing Wu, Joseph JaJa

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Tags: CUDA, FFT, Heterogeneous systems, nVidia, Tesla C1060

April 13, 2013 by hgpu

Modeling of High Performance Programs to Support Heterogeneous Computing

Ferosh Jacob

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Tags: Computer science, CUDA, Heterogeneous systems, MapReduce, nVidia, OpenCL, Software Engineering, Tesla M2070, Thesis

April 9, 2013 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

On the Efficacy of GPU-Integrated MPI for Scientific Applications

Programming Models and Runtimes for Heterogeneous Systems

Valar: A Benchmark Suite to Study the Dynamic Behavior of Heterogeneous Systems

An Automatic Input-Sensitive Approach for Heterogeneous Task Partitioning

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

Supporting Iteration in a Heterogeneous Data Flow Engine

A Framework for Profiling and Performance Monitoring of Heterogeneous Applications

Co-processing SPMD Computation on GPUs and CPUs on Shared Memory System

High Performance FFT Based Poisson Solver on a CPU-GPU Heterogeneous Platform

Modeling of High Performance Programs to Support Heterogeneous Computing

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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