high performance computing on graphics processing units: hgpu.org

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Performance Evaluation of Intel Xeon Phi Coprocessor using XKaapi

Joao V. F. Lima, Nicolas Maillard, Franciois Broquedis, Thierry Gautier, Bruno Raffin

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

August 26, 2013 by hgpu

Reverse Computation for Rollback-based Fault Tolerance in Large Parallel Systems: Evaluating the Potential Gains and Systems Effects

Kalyan S. Perumalla, Alfred J. Park

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 580, Particle simulation, Performance, Tesla X2090

August 24, 2013 by hgpu

Comparative Analysis of OpenACC, OpenMP and CUDA using Sequential and Parallel Algorithms

Cleverson Lopes Ledur, Carlos M. D. Zeve, Julio C. S. dos Anjos

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 650, OpenACC, OpenMP, Performance

August 21, 2013 by hgpu

Comparison of Cilk, Kaapi and CUDA for the Jacobi Method

Luis Felipe Millani, Nicolas Maillard

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Performance

August 21, 2013 by hgpu

Accurate Analytic Models to Estimate Execution Time on GPU Applications

Pedro Velho, Daniel A. G. de Oliveira, Edson L. Padoin, Philippe O. A. Navaux

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Performance, Programming techniques

August 21, 2013 by hgpu

Performance Drawbacks for Matrix Multiplication using Set Associative Cache in GPU devices

Leonid Djinevski, Sime Arsenovski, Sasko Ristov, Marjan Gusev

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Tags: Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce GTX 680, Performance

August 19, 2013 by hgpu

Transfer Time Reduction of Data Transfers between CPU and GPU

Julius Sandgren

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Tags: ATI, ATI Radeon HD 3650, Computer science, CUDA, nVidia, nVidia Quadro FX 370, OpenCL, Performance, Thesis

August 19, 2013 by hgpu

A GPU implementation for improved granular simulations with LAMMPS

Emmanuel N. Millan, Christian Ringl, Carlos S. Bederian, Maria Fabiana Piccoli, Carlos Garcia Garino, Herbert M. Urbassek, Eduardo M. Bringa

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Tags: CUDA, Molecular dynamics, nVidia, Performance, Physics, Tesla C2050, Tesla C2075, Tesla M2090

August 18, 2013 by hgpu

General Transformations for GPU Execution of Tree Traversals

Michael Goldfarb, Youngjoon Jo, Milind Kulkarni

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Tags: Algorithms, Compilers, Computer science, CUDA, KD-tree, nVidia, Performance, Tesla C2070

August 15, 2013 by hgpu

First experiences with the Intel MIC architecture at LRZ

Volker Weinberg, Momme Allalen

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

August 15, 2013 by hgpu

Barrier Invariants: A Shared State Abstraction for the Analysis of Data-Dependent GPU Kernels

Nathan Chong, Alastair F. Donaldson, Paul H.J. Kelly, Jeroen Ketema, Shaz Qadeer

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Tags: Algorithms, Computer science, CUDA, OpenCL, Package, Performance

August 13, 2013 by hgpu

Scheduling by Work-Stealing in Hybrid Parallel Architectures

Vinicius Garcia Pinto, Nicolas Maillard

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Performance, Task scheduling

July 24, 2013 by hgpu

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

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

Agentic Code Optimization via Compiler-LLM Cooperation

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

LLM.Q: Quantized LLM training in pure CUDA/C++

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

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high performance computing on graphics processing units: hgpu.org

Performance Evaluation of Intel Xeon Phi Coprocessor using XKaapi

Reverse Computation for Rollback-based Fault Tolerance in Large Parallel Systems: Evaluating the Potential Gains and Systems Effects

Comparative Analysis of OpenACC, OpenMP and CUDA using Sequential and Parallel Algorithms

Comparison of Cilk, Kaapi and CUDA for the Jacobi Method

Accurate Analytic Models to Estimate Execution Time on GPU Applications

Performance Drawbacks for Matrix Multiplication using Set Associative Cache in GPU devices

Transfer Time Reduction of Data Transfers between CPU and GPU

A GPU implementation for improved granular simulations with LAMMPS

General Transformations for GPU Execution of Tree Traversals

First experiences with the Intel MIC architecture at LRZ

Barrier Invariants: A Shared State Abstraction for the Analysis of Data-Dependent GPU Kernels

Scheduling by Work-Stealing in Hybrid Parallel Architectures

Recent source codes

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CUDA Kernel Fusion Benchmarks

IntelliKit: Agent-first tooling for AMD hardware

DITRON: Distributed Compiler based on Triton for Parallel Systems

CuTile Benchmark Suite: Performance and Productivity Tradeoffs for GPU Kernel Programming on Blackwell Architecture

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

Device Virtual Machine (DVM)

Agentic Code Optimization via Compiler-LLM Cooperation

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

LLM.Q: Quantized LLM training in pure CUDA/C++

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