high performance computing on graphics processing units: hgpu.org

Arvind K. Sujeeth, Tiark Rompf, Kevin J. Brown, HyoukJoong Lee, Hassan Chafi, Victoria Popic, Michael Wu, Aleksander Prokopec, Vojin Jovanovic, Martin Odersky, Kunle Olukotun

View

Download (PDF)

Source codes

Tags: Computer science, CUDA, MPI, nVidia, Package, Performance, Tesla C2050

May 31, 2013 by hgpu

Geometric Optimisation using Karva for Graphical Processing Units

A.V. Husselmann, K.A. Hawick

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Genetic programming, nVidia, Particle swarm optimization

May 31, 2013 by hgpu

GPU Multiple Sequence Alignment Fourier-Space Cross-Correlation Alignment

Matthias Alexander Lee

View

Download (PDF)

Source codes

Tags: Bioinformatics, Biology, Computational Complexity, CUDA, nVidia, Package, Python, RNA, Sequence alignment, Signal processing

May 31, 2013 by hgpu

Improved Performance of CaFE and IRIS Model Fitting Using CUDA

Dylan Jackson

View

Download (PDF)

Tags: Biology, Biomolecules, CUDA, nVidia, nVidia GeForce GTX 680, Sensing, Thesis

May 31, 2013 by hgpu

Tiling optimizations for stencil computations

Xing Zhou

View

Download (PDF)

Tags: Computer science, nVidia, nVidia GeForce GTX 480, Optimization, Performance, Thesis

May 31, 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

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

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

Applications

GPU Acceleration of a Genetic Algorithm for the Synthesis of FSM-based Bimodal Predictors

CUDA Leaks: Information Leakage in GPU Architectures

GPU-accelerated generation of correctly-rounded elementary functions

GPUburn: A System to Test and Mitigate GPU Hardware Failures

Novel Multi-Layer Network Decomposition Boosting Acceleration of Multi-core Algorithms

On GPU Fourier Transformations

Task scheduling in hybrid CPU-GPU systems

Composition and Reuse with Compiled Domain-Specific Languages

Geometric Optimisation using Karva for Graphical Processing Units

GPU Multiple Sequence Alignment Fourier-Space Cross-Correlation Alignment

Improved Performance of CaFE and IRIS Model Fitting Using CUDA

Tiling optimizations for stencil computations

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

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

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

Most viewed papers (last 30 days)