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Efficient Sparse Matrix-Vector Multiplication on GPUs using the CSR Storage Format

Joseph L. Greathouse, Mayank Daga

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Tags: Algorithms, AMD FirePro W9100, ATI, Computer science, Linear Algebra, OpenCL, Sparse matrix

November 4, 2015 by hgpu

Performance of GTX Titan X GPUs and Code Optimization

Hwancheol Jeong, Sangbaek Lee, Weonjong Lee, Jeonghwan Pak, Jangho Kim, Juhyun Chung

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX Titan X, Performance

November 4, 2015 by hgpu

Accelerating Twisted Mass LQCD with QPhiX

Mario Schrock, Silvano Simula, Alexei Strelchenko

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Tags: High Energy Physics - Lattice, Intel Xeon Phi, Package, Physics, QCD

November 4, 2015 by hgpu

Exact diagonalization of quantum lattice models on coprocessors

Topi Siro, Ari Harju

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Tags: Condensed matter, CUDA, Intel Xeon Phi, Mathematical Software, nVidia, OpenMP, Physics, Tesla K40

November 4, 2015 by hgpu

Heterogeneous CPU/(GP) GPU Memory Hierarchy Analysis and Optimization

Josue Vladimir Quiroga Esparza

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Tags: Computer science, CUDA, Heterogeneous systems, Memory, nVidia, Thesis

November 4, 2015 by hgpu

Structural Agnostic SpMV: Adapting CSR-Adaptive for Irregular Matrices

Mayank Daga, Joseph L. Greathouse

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Tags: Algorithms, AMD FirePro W9100, APU, ATI, Computer science, Linear Algebra, OpenCL, Package, Sparse matrix

November 3, 2015 by hgpu

Software Defined Radio over CUDA

Marco Ribero

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Tags: CUDA, nVidia, nVidia GeForce GT 540 M, Signal processing, Thesis

November 3, 2015 by hgpu

On the programmability of multi-GPU computing systems

Javier Cabezas Rodriguez

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Tags: Computer science, CUDA, MPI, nVidia, Performance, Tesla C1060, Tesla K20, Tesla K40, Thesis

November 3, 2015 by hgpu

Exploring Optimisations for the Local Assembly phase of Finite Element Methods on GPUs

Hector Dearman

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Tags: Benchmarking, Computer science, CUDA, Differential equations, FEM, Finite element method, nVidia, nVidia GRID K520, OpenCL, Partial differential equations, PDEs, Thesis

November 3, 2015 by hgpu

A Framework for Transparent Execution of Massively-Parallel Applications on CUDA and OpenCL

Jorn Teuber, Rene Weller, Gabriel Zachmann

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 680, OpenCL

November 3, 2015 by hgpu

Estimation of numerical reproducibility on CPU and GPU

Fabienne Jezequel, Jean-Luc Lamotte, Issam Said

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Tags: APU, ATI, ATI Radeon HD 7970, Computer science, CUDA, nVidia, OpenCL, Tesla C2050, Tesla K20

October 31, 2015 by hgpu

Parallelization of Encryption and Hashing Algorithm Using GPU

Vijay J.Bodake, D.B.Bangal, K.B.Dhatrak

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Tags: Computer science, CUDA, Hashing, nVidia, nVidia GeForce GTX 680, Security

October 31, 2015 by hgpu

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

* * *

high performance computing on graphics processing units: hgpu.org

Applications

Efficient Sparse Matrix-Vector Multiplication on GPUs using the CSR Storage Format

Performance of GTX Titan X GPUs and Code Optimization

Accelerating Twisted Mass LQCD with QPhiX

Exact diagonalization of quantum lattice models on coprocessors

Heterogeneous CPU/(GP) GPU Memory Hierarchy Analysis and Optimization

Structural Agnostic SpMV: Adapting CSR-Adaptive for Irregular Matrices

Software Defined Radio over CUDA

On the programmability of multi-GPU computing systems

Exploring Optimisations for the Local Assembly phase of Finite Element Methods on GPUs

A Framework for Transparent Execution of Massively-Parallel Applications on CUDA and OpenCL

Estimation of numerical reproducibility on CPU and GPU

Parallelization of Encryption and Hashing Algorithm Using GPU

Recent source codes

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

True 4-Bit Quantized CNN Training on CPU

cuFuzz: A GPU-oriented coverage-guided fuzzer for userland CUDA application

KernelSkill: A Multi-Agent Framework for GPU Kernel Optimization

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