high performance computing on graphics processing units: hgpu.org

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Pseudorandom Numbers Generation for Monte Carlo Simulations on GPUs: OpenCL Approach

Vadim Demchik

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Tags: ATI, ATI Radeon HD 5870, ATI Radeon HD 6970, Computer science, Monte Carlo simulation, nVidia, nVidia GeForce GTX 560, nVidia GeForce GTX 560 M, nVidia GeForce GTX 780, OpenCL, Package, Pseudo-random number generators, Review, Tesla K40

March 25, 2015 by hgpu

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

Johannes Koster

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Tags: Algorithms, Bayesian, Bioinformatics, Biology, Filtering, Next-Generation sequencing, nVidia, nVidia GeForce GTX 580, nVidia GeForce GTX 780, OpenCL, Package, Thesis

March 23, 2015 by hgpu

GPU Kernels for High-Speed 4-Bit Astrophysical Data Processing

Peter Klages, Kevin Bandura, Nolan Denman, Andre Recnik, Jonathan Sievers, Keith Vanderlinde

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Tags: AMD Radeon R9 280X, Astrophysics, ATI, Instrumentation and Methods for Astrophysics, OpenCL, Package

March 23, 2015 by hgpu

CSR5: An Efficient Storage Format for Cross-Platform Sparse Matrix-Vector Multiplication

Weifeng Liu, Brian Vinter

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Tags: Algorithms, AMD Radeon R9 290X, Benchmarking, Computer science, CUDA, Intel Xeon Phi, Mathematical Software, nVidia, nVidia GeForce GTX 980, OpenCL, Package, Performance, Sparse matrix

March 20, 2015 by hgpu

Implementation of a Practical Distributed Calculation System with Browsers and JavaScript, and Application to Distributed Deep Learning

Ken Miura, Tatsuya Harada

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Tags: Algorithms, Computer science, Java, Machine learning, Neural and Evolutionary Computing, Neural networks, nVidia, nVidia GeForce GTX Titan, OpenCL, Package, WebCL

March 20, 2015 by hgpu

Fast Sparse Matrix Multiplication on GPU

Lukas Polok, Viorela Ila, Pavel Smrz

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Tags: Algorithms, Computer science, CUDA, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 680, OpenCL, Package, Sorting, Sparse matrix, Tesla K40

March 18, 2015 by hgpu

HELIOS-K: An Ultrafast, Open-source Opacity Calculator for Radiative Transfer

Simon L. Grimm, Kevin Heng

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Tags: Astrophysics, CUDA, Earth and Planetary Astrophysics, nVidia, Package, Tesla K20

March 14, 2015 by hgpu

Counting Triangles in Large Graphs on GPU

Adam Polak

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Tags: Clustering, Computer science, CUDA, Graph theory, MapReduce, nVidia, Package, Tesla C2050

March 3, 2015 by hgpu

MILJS: Brand New JavaScript Libraries for Matrix Calculation and Machine Learning

Ken Miura, Tetsuaki Mano, Atsushi Kanehira, Yuichiro Tsuchiya, Tatsuya Harada

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Tags: Computer science, Java, Machine learning, Mathematical Software, OpenCL, Package, Python

March 2, 2015 by hgpu

Implementation of Smith-Waterman algorithm in OpenCL for GPUs

Dzmitry Razmyslovich, Guillermo Marcus, Markus Gipp, Marc Zapatka, Andreas Szillus

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Tags: Bioinformatics, Biology, nVidia, nVidia GeForce GTX 480, OpenCL, Package, Sequence alignment, Smith-Waterman algorithm

February 27, 2015 by azhar ali

RSVDPACK: Subroutines for computing partial singular value decompositions via randomized sampling on single core, multi core, and GPU architectures

Sergey Voronin, Per-Gunnar Martinsson

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Tags: Algorithms, CUDA, CULA, Mathematical Software, Mathematics, Matrix decomposition, Numerical Analysis, nVidia, Package, Tesla M2070

February 22, 2015 by hgpu

Exploring Design Space of 3D NVM and eDRAM Caches Using DESTINY Tool (open-source code)

Sparsh Mittal, Matt Poremba, Jeffrey Vetter, Yuan Xie

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Tags: Architecture, area, cache, cpu, energy, GPGPU, latency modeling, Memory, Package

February 22, 2015 by sparsh0mittal

CFAL-bench

Comparing Parallel Functional Array Languages: Programming and Performance

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

DeepCompile: A Compiler-Driven Approach to Optimizing Distributed Deep Learning Training

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

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high performance computing on graphics processing units: hgpu.org

Pseudorandom Numbers Generation for Monte Carlo Simulations on GPUs: OpenCL Approach

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

GPU Kernels for High-Speed 4-Bit Astrophysical Data Processing

CSR5: An Efficient Storage Format for Cross-Platform Sparse Matrix-Vector Multiplication

Implementation of a Practical Distributed Calculation System with Browsers and JavaScript, and Application to Distributed Deep Learning

Fast Sparse Matrix Multiplication on GPU

HELIOS-K: An Ultrafast, Open-source Opacity Calculator for Radiative Transfer

Counting Triangles in Large Graphs on GPU

MILJS: Brand New JavaScript Libraries for Matrix Calculation and Machine Learning

Implementation of Smith-Waterman algorithm in OpenCL for GPUs

RSVDPACK: Subroutines for computing partial singular value decompositions via randomized sampling on single core, multi core, and GPU architectures

Exploring Design Space of 3D NVM and eDRAM Caches Using DESTINY Tool (open-source code)

Recent source codes

CFAL-bench

Efficient Graph Embedding at Scale: Optimizing CPU-GPU-SSD Integration

Can Large Language Models Predict Parallel Code Performance?

PELSI: Power-Efficient Layer-Switched Inference

Ouroboros: Virtualized Queues for dynamic memory management

MSCCL++: A GPU-driven communication stack for scalable AI applications

Benchmark compute shader of Unity against InteropUnityCUDA

Data-efficient LLM Fine-tuning for Code Generation

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

Large Language Model Powered C-to-CUDA Code Translation: A Novel Auto-Parallelization Framework

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