high performance computing on graphics processing units: hgpu.org

hgpu.org » Performance

Modeling and Simulation of a Dynamic Task-Based Runtime System for Heterogeneous Multi-Core Architectures

Luka Stanisic, Samuel Thibault, Arnaud Legrand, Brice Videau, Jean-Francois Mehaut

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Tags: Computer science, CUDA, Heterogeneous systems, Linear Algebra, nVidia, nVidia Quadro FX 5800, Performance, Task scheduling, Tesla C2050, Tesla K20, Tesla M2075

April 9, 2014 by hgpu

A New Non-Blocking Approach on GPU Dynamical Memory Management

Yu-Shiang Lin, Chun-Yuan Lin, Jon-Yu Lee

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Tags: Computer science, CUDA, Memory model, nVidia, Performance, Tesla K20

April 7, 2014 by hgpu

Acceleration of a Full-scale Industrial CFD Application with OP2

Istvan Z. Reguly, Gihan R. Mudalige, Carlo Bertolli, Michael B. Giles, Adam Betts, Paul H. J. Kelly, David Radford

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Tags: Computer science, CUDA, Fluid dynamics, MPI, nVidia, Performance, Tesla K20

April 7, 2014 by hgpu

Improving Cache Locality for GPU-based Volume Rendering

Y. Sugimoto, F. Ino, K. Hagihara

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Tags: CUDA, nVidia, nVidia GeForce GTX 580, OpenCL, Performance, Rendering

March 28, 2014 by hgpu

Demonstrating Self-Learning Algorithm Adaptivity in a Hardware-Oblivious Database Engine

M. Heimel, F. Haase, M. Meinke, S. Breß, M. Saecker, V. Markl

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Tags: Databases, Heterogeneous systems, OpenCL, Performance

March 24, 2014 by hgpu

Dynamic Load Balancing using Graphics Processors

R. Mohan, N. P. Gopalan

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Tags: Computer science, CUDA, nVidia, Performance, Task scheduling

March 18, 2014 by hgpu

Interactive Program Debugging and Optimization for Directive-Based, Efficient GPU Computing

Seyong Lee, Dong Li, Jeffrey S. Vetter

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Tags: Benchmarking, Computer science, CUDA, nVidia, OpenACC, Performance, Tesla M2090

March 7, 2014 by hgpu

Code Optimization and Scaling of the Astrophysics Software Gadget on Intel Xeon Phi

P. Borovska, D. Ivanova

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Tags: Astrophysics, Intel Xeon Phi, MPI, N-body simulation, Performance

March 6, 2014 by hgpu

Performance Tradeoff Spectrum of Integer and Floating Point Applications Kernels on Various GPUs

M.G.B. Johnson, D. P. Playne, K.A. Hawick

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Tags: Benchmarking, Computer science, CUDA, nVidia, nVidia GeForce GTX 260, nVidia GeForce GTX 480, nVidia GeForce GTX 580, nVidia GeForce GTX 590, nVidia GeForce GTX 660 M, nVidia GeForce GTX 680, Performance, Tesla M2050, Tesla M2070, Tesla M2075, Tesla M2090

March 6, 2014 by hgpu

Performance Analysis for GPU-based Ray-triangle Algorithms

Juan J. Jimenez, Carlos J. Ogayar, Jose M. Noguera, Felix Paulano

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Tags: 3D Graphics and Realism, Computer science, CUDA, nVidia, nVidia GeForce GTX 560 Ti, Performance, Raytracing

March 6, 2014 by hgpu

Increasing predictability of GPU’s

David Defour

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

March 3, 2014 by hgpu

Exploitation of GPUs for the Parallelisation of Probably Parallel Legacy Code

Zheng Wang, Daniel Powell, Bjorn Franke, Michael O'Boyle

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Tags: Computer science, nVidia, nVidia GeForce GTX 580, OpenCL, Performance

February 27, 2014 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

Modeling and Simulation of a Dynamic Task-Based Runtime System for Heterogeneous Multi-Core Architectures

A New Non-Blocking Approach on GPU Dynamical Memory Management

Acceleration of a Full-scale Industrial CFD Application with OP2

Improving Cache Locality for GPU-based Volume Rendering

Demonstrating Self-Learning Algorithm Adaptivity in a Hardware-Oblivious Database Engine

Dynamic Load Balancing using Graphics Processors

Interactive Program Debugging and Optimization for Directive-Based, Efficient GPU Computing

Code Optimization and Scaling of the Astrophysics Software Gadget on Intel Xeon Phi

Performance Tradeoff Spectrum of Integer and Floating Point Applications Kernels on Various GPUs

Performance Analysis for GPU-based Ray-triangle Algorithms

Increasing predictability of GPU’s

Exploitation of GPUs for the Parallelisation of Probably Parallel Legacy Code

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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