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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

The More We Share, The More We Have: Improving GPU performance through Register Sharing

Vishwesh Jatala, Jayvant Anantpur, Amey Karkare

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Tags: Computer science, CUDA, GPGPU-sim, Hardware Architecture, nVidia, Performance

March 20, 2015 by hgpu

An Empirical Performance Evaluation of GPU-Enabled Graph-Processing Systems

Yong Guo, Ana Lucia Varbanescu, Alexandru Iosup, Dick Epema

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 480, nVidia GeForce GTX 580, Performance, Tesla K20

March 8, 2015 by hgpu

HPerf: A Lightweight Profiler for Task Distribution on CPU+GPU Platforms

Joo Hwan Lee, Nimit Nigania, Hyesoon Kim, Bevin Brett

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Tags: Computer science, Heterogeneous systems, nVidia, nVidia Quadro 2000, OpenCL, Performance

March 8, 2015 by hgpu

Stochastic Gradient Descent on GPUs

Rashid Kaleem, Sreepathi Pai, Keshav Pingali

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Tags: Algorithms, Computer science, Machine learning, nVidia, OpenCL, Performance, Tesla K40

February 22, 2015 by hgpu

Comparison of SPMV performance on matrices with different matrix format using CUSP, cuSPARSE and ViennaCL

Ang Li, Hammad Mazhar, Radu Serban, Dan Negrut

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Tags: Computer science, CUDA, Linear Algebra, nVidia, OpenCL, Performance, Python, Sparse matrix, Tesla K20

February 22, 2015 by hgpu

Patterns and Rewrite Rules for Systematic Code Generation (From High-Level Functional Patterns to High-Performance OpenCL Code)

Michel Steuwer, Christian Fensch, Christophe Dubach

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Tags: ATI, ATI Radeon HD 7970, Code generation, Computer science, nVidia, nVidia GeForce GTX 480, OpenCL, Performance, Programming Languages

February 10, 2015 by hgpu

Comparison of OpenCL performance on different platforms using VexCL and Blaze

Hammad Mazhar, Dan Negrut

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Tags: AMD Radeon R7 260, ATI, Benchmarking, Computer science, Intel Xeon Phi, nVidia, nVidia GeForce GTX 680, nVidia GeForce GTX 770, OpenCL, Performance, Sparse matrix, Tesla K20

February 6, 2015 by hgpu

Characterizing and Enhancing Global Memory Data Coalescing on GPUs

Naznin Fauzia, Louis-Noel Pouchet, P. Sadayappan

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Tags: Computer science, CUDA, Memory, nVidia, Performance, PTX, Tesla K20

February 2, 2015 by hgpu

Different Optimization Strategies and Performance Evaluation of Reduction on Multicore CUDA Architecture

Chhaya Patel

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Tags: Algorithms, Computer science, CUDA, nVidia, nVidia GeForce GTX 480, Performance

January 30, 2015 by hgpu

The GPU vs Phi Debate: Risk Analytics Using Many-Core Computing

Blesson Varghese

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Tags: Computational Engineering, Computer science, CUDA, Finance, Intel Xeon Phi, nVidia, Performance, Tesla C2075

January 28, 2015 by hgpu

GPU computing architecture for irregular parallelism

Wilson Wai Lun Fung

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Tags: Computer science, CUDA, GPGPU-sim, nVidia, nVidia GeForce GTX 480, Performance, Thesis

January 26, 2015 by hgpu

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Pinocchio: PINpointing Orbit Crossing Collapsed Hierarchical Objects

Accelerating cosmological simulations on GPUs: a portable approach using OpenMP

KernelCoder: trained on a curated dataset of reasoning traces and CUDA kernel pairs

ConCuR: Conciseness Makes State-of-the-Art Kernel Generation

VibeCodeHPC - Multi Agentic Vibe Coding for HPC

VibeCodeHPC: An Agent-Based Iterative Prompting Auto-Tuner for HPC Code Generation Using LLMs

Compile-Time Resource Safety for GPU APIs: A Low-Overhead Typestate Framework

* * *

high performance computing on graphics processing units: hgpu.org

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

The More We Share, The More We Have: Improving GPU performance through Register Sharing

An Empirical Performance Evaluation of GPU-Enabled Graph-Processing Systems

HPerf: A Lightweight Profiler for Task Distribution on CPU+GPU Platforms

Stochastic Gradient Descent on GPUs

Comparison of SPMV performance on matrices with different matrix format using CUSP, cuSPARSE and ViennaCL

Patterns and Rewrite Rules for Systematic Code Generation (From High-Level Functional Patterns to High-Performance OpenCL Code)

Comparison of OpenCL performance on different platforms using VexCL and Blaze

Characterizing and Enhancing Global Memory Data Coalescing on GPUs

Different Optimization Strategies and Performance Evaluation of Reduction on Multicore CUDA Architecture

The GPU vs Phi Debate: Risk Analytics Using Many-Core Computing

GPU computing architecture for irregular parallelism

Recent source codes

Interleaved Learning and Exploration: A Self-Adaptive Fuzz Testing Framework for MLIR

Pinocchio: PINpointing Orbit Crossing Collapsed Hierarchical Objects

KernelCoder: trained on a curated dataset of reasoning traces and CUDA kernel pairs

VibeCodeHPC - Multi Agentic Vibe Coding for HPC

Compile-Time Resource Safety for GPU APIs: A Low-Overhead Typestate Framework

exa-AMD: Exascale Accelerated Materials Discovery

TRUST: a thermalhydraulic software package for CFD simulations

Modular: The Modular Platform (includes MAX & Mojo)

Allo: Accelerator Design Language

Towards Robust Agentic CUDA Kernel Benchmarking, Verification, and Optimization

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