high performance computing on graphics processing units: hgpu.org

Ruibang Luo, Thomas Wong, Jianqiao Zhu, Chi-Man Liu, Edward Wu, Lap-Kei Lee, Haoxiang Lin, Wenjuan Zhu, David W. Cheung, Hing-Fung Ting, Siu-Ming Yiu, Chang Yu, Yingrui Li, Ruiqiang Li, Tak-Wah Lam

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Tags: Algorithms, Bioinformatics, Biology, CUDA, Next-Generation sequencing, nVidia, Package, Tesla C2070, Tesla M2050

February 25, 2013 by hgpu

G-SNPM – A GPU-based SNP mapping tool

Alessandro Orro, Andrea Manconi, Emanuele Manca, Giuliano Armano, Luciano Milanesi

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Tags: Algorithms, Bioinformatics, Biology, CUDA, Next-Generation sequencing, nVidia, nVidia GeForce GTX 480, Package, Python, Smith-Waterman algorithm

November 14, 2012 by hgpu

GHOSTM: A GPU-Accelerated Homology Search Tool for Metagenomics

Shuji Suzuki, Takashi Ishida, Ken Kurokawa, Yutaka Akiyama

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Tags: Algorithms, Biology, CUDA, Databases, Next-Generation sequencing, nVidia, Package, Tesla S1070

May 10, 2012 by hgpu

Exact and complete short-read alignment to microbial genomes using Graphics Processing Unit programming

Jochen Blom, Tobias Jakobi, Daniel Doppmeier, Sebastian Jaenicke, Jorn Kalinowski, Jens Stoye, Alexander Goesmann

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Tags: Algorithms, Bioinformatics, Biology, CUDA, Next-Generation sequencing, nVidia, Package

May 10, 2011 by hgpu

Exact and complete short read alignment to microbial genomes using GPU programming

Jochen Blom, Tobias Jakobi, Daniel Doppmeier, Sebastian Jaenicke, Jorn Kalinowski, Jens Stoye, Alexander Goesmann

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Tags: Bioinformatics, Biology, CUDA, Next-Generation sequencing, nVidia, nVidia GeForce GTX 280, Package

April 11, 2011 by hgpu

DecGPU: distributed error correction on massively parallel graphics processing units using CUDA and MPI

Yongchao Liu, Bertil Schmidt, Douglas Maskell

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Tags: Bioinformatics, Biology, CUDA, MPI, Next-Generation sequencing, nVidia, OpenMP, Package, Tesla S1070

April 11, 2011 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

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

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high performance computing on graphics processing units: hgpu.org

AnySeq/GPU: A Novel Approach for Faster Sequence Alignment on GPUs

eccCL: parallelized GPU implementation of Ensemble Classifier Chains

Parallelization, Scalability, and Reproducibility in Next-Generation Sequencing Analysis

SW#db: GPU-accelerated exact sequence similarity database search

GPU-accelerated Computation for Statistical Analysis of the Next-Generation Sequencing Data

cuBLASTP: Fine-Grained Parallelization of Protein Sequence Search on a GPU

SOAP3-dp: Fast, Accurate and Sensitive GPU-based Short Read Aligner

G-SNPM – A GPU-based SNP mapping tool

GHOSTM: A GPU-Accelerated Homology Search Tool for Metagenomics

Exact and complete short-read alignment to microbial genomes using Graphics Processing Unit programming

Exact and complete short read alignment to microbial genomes using GPU programming

DecGPU: distributed error correction on massively parallel graphics processing units using CUDA and MPI

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

SOL-ExecBench: Speed-of-Light Benchmarking for Real-World GPU Kernels Against Hardware Limits

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