high performance computing on graphics processing units: hgpu.org

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Parallel accelerators for GlimmerHMM bioinformatics algorithm

Nafsika Chrysanthou, Grigorios Chrysos, Euripides Sotiriades, Ioannis Papaefstathiou

Tags: Algorithms, Bioinformatics, Biology, FPGA, Genetics

June 8, 2011 by hgpu

Highly efficient mapping of the Smith-Waterman algorithm on CUDA-compatible GPUs

K. Dohi, K. Benkridt, C. Ling, T. Hamada, Y. Shibata

Tags: Bioinformatics, Biology, CUDA, nVidia, Sequence alignment, Smith-Waterman algorithm

May 31, 2011 by hgpu

Fast Parallel Markov Clustering in Bioinformatics Using Massively Parallel Graphics Processing Unit Computing

Alhadi Bustamam, Kevin Burrage, Nicholas A. Hamilton

Tags: Bioinformatics, Biology, CUDA, MPI, nVidia, Sparse matrix

May 30, 2011 by hgpu

Fast network centrality analysis using GPUs

Zhiao Shi, Bing Zhang

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Tags: All-Pairs Distance, Bioinformatics, Biology, CUDA, nVidia, Package, Tesla C2050

May 13, 2011 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

GPU Based Parallel Computing on Blast Program

Huang Lican, Hu Ya

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Tags: Bioinformatics, Biology, BLAST, Sequence alignment

May 7, 2011 by hgpu

GPU acceleration for statistical gene classification

Alfredo Benso, Stefano Di Carlo, Gianfranco Politano, Alessandro Savino

Tags: Bioinformatics, Biology, CUDA, Genetics, nVidia

May 5, 2011 by hgpu

Accelerating scientific applications using GPU’s

M. Taher

Tags: Bioinformatics, Biology, Computer science, CUDA, FFT, nVidia, Security, Sequence alignment

May 3, 2011 by hgpu

Accelerating DNA analysis applications on GPU clusters

A. Tumeo, O. Villa

Tags: Bioinformatics, Biology, GPU cluster, MPI, nVidia, Tesla C1060

April 28, 2011 by hgpu

Pathological Image Analysis Using the GPU: Stroma Classification for Neuroblastoma

Antonio Ruiz, Olcay Sertel, Manuel Ujaldon, Umit Catalyurek, Joel Saltz, Metin Gurcan

Tags: Algorithms, Bioinformatics, Image processing, Medicine

April 26, 2011 by hgpu

Parallel Approaches for SWAMP Sequence Alignment

Shannon Steinfadt, Kevin Schaffer

Tags: Bioinformatics, Biology, Cell processor, FPGA, nVidia, Sequence alignment, Smith-Waterman algorithm

April 19, 2011 by hgpu

CUDA-BLASTP: Accelerating BLASTP on CUDA-Enabled Graphics Hardware

Weiguo Liu, Bertil Schmidt, Wolfgang Muller-Wittig

Source codes

Tags: Bioinformatics, Biology, BLAST, CUDA, nVidia, nVidia GeForce GTX 295, Package, Sequence alignment

April 18, 2011 by hgpu

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

BoltzGen:Toward Universal Binder Design

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

See all packages

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high performance computing on graphics processing units: hgpu.org

Parallel accelerators for GlimmerHMM bioinformatics algorithm

Highly efficient mapping of the Smith-Waterman algorithm on CUDA-compatible GPUs

Fast Parallel Markov Clustering in Bioinformatics Using Massively Parallel Graphics Processing Unit Computing

Fast network centrality analysis using GPUs

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

GPU Based Parallel Computing on Blast Program

GPU acceleration for statistical gene classification

Accelerating scientific applications using GPU’s

Accelerating DNA analysis applications on GPU clusters

Pathological Image Analysis Using the GPU: Stroma Classification for Neuroblastoma

Parallel Approaches for SWAMP Sequence Alignment

CUDA-BLASTP: Accelerating BLASTP on CUDA-Enabled Graphics Hardware

Recent source codes

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

BoltzGen:Toward Universal Binder Design

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

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