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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

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

Reproducible Triangular Solvers for High-Performance Computing

Roman Iakymchuk, David Defour, Sylvain Collange, Stef Graillat

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Tags: Algorithms, Computer science, Linear Algebra, nVidia, nVidia Quadro K5000, OpenCL, Package, Tesla K20

February 19, 2015 by hgpu

Memory-efficient Adaptive Subdivision for Software Rendering on the GPU

Thomas Weber

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Tags: ATI, ATI Radeon HD 7970, Computer science, OpenCL, Package, Rendering, Tessellation

February 19, 2015 by hgpu

NUPAR: A Benchmark Suite for Modern GPU Architectures

Yash Ukidave, Fanny Nina Paravecino, Leiming Yu, Charu Kalra, Amir Momeni, Zhongliang Chen, Nick Materise, Brett Daley, Perhaad Mistry, David Kaeli

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Tags: ATI, ATI Radeon HD 7970, Benchmarking, Computer science, CUDA, Heterogeneous systems, nVidia, OpenACC, OpenCL, Package, Tesla C2070, Tesla K40

February 13, 2015 by hgpu

FSCL: Homogeneous programming, scheduling and execution on heterogeneous platforms

Gabriele Cocco

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Tags: ATI, ATI Radeon HD 7970, Computer science, Heterogeneous systems, OpenCL, Package, Thesis

February 10, 2015 by hgpu

IntelliKit: Agent-first tooling for AMD hardware

Kerncap: Automated Kernel Extraction and Isolation for AMD GPUs

DITRON: Distributed Compiler based on Triton for Parallel Systems

DITRON: Distributed Multi-level Tiling Compiler for Parallel Tensor Programs

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

LLM.Q: Quantized LLM training in pure CUDA/C++

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

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

Programming

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

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

Implementation of Smith-Waterman algorithm in OpenCL for GPUs

Reproducible Triangular Solvers for High-Performance Computing

Memory-efficient Adaptive Subdivision for Software Rendering on the GPU

NUPAR: A Benchmark Suite for Modern GPU Architectures

FSCL: Homogeneous programming, scheduling and execution on heterogeneous platforms

Recent source codes

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

LLM.Q: Quantized LLM training in pure CUDA/C++

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

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