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Intel Xeon Phi Coprocessor High-Performance Programming

Jim Jeffers, James Jeffers, James Reinders

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Tags: Book, Computer science, Intel, Intel Phi, OpenMP, Package, Review

June 30, 2013 by hgpu

Efficient computation of constrained parameterizations on parallel platforms

Theodoros Athanasiadis, Georgios Zioupos, Ioannis Fudos

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Tags: 3D Graphics and Realism, Computer science, nVidia, OpenCL, Package, Tesla C2070

June 29, 2013 by hgpu

libCudaOptimize: an Open Source Library of GPU-based Metaheuristics

Youssef S.G. Nashed, Roberto Ugolotti, Pablo Mesejo, Stefano Cagnoni

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Tags: Artificial intelligence, Computer science, CUDA, Differential evolution, Evolutionary Computations, Metaheuristics, Neural and Evolutionary Computing, nVidia, nVidia GeForce GTS 450, Optimization, Package, Particle swarm optimization

June 21, 2013 by hgpu

CFMDS: CUDA-based fast multidimensional scaling for genome-scale data

Sungin Park, Soo-Yong Shin, Kyu-Baek Hwang

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Tags: Bioinformatics, Biology, CUDA, Genetics, Multidimensional scaling, nVidia, Package

June 21, 2013 by hgpu

Delaunay Triangulation in R3 on the GPU

Ashwin Nanjappa

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, nVidia, nVidia GeForce GTX 580, Package, Thesis, Voronoi diagram

June 18, 2013 by hgpu

Bayesian State-Space Modelling on High-Performance Hardware Using LibBi

Lawrence M. Murray

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Tags: Bayesian, CUDA, Mathematics, nVidia, Package, Statistics, Tesla C2075, Tesla S2050

June 17, 2013 by hgpu

GPU Programming in Rust: Implementing High Level Abstractions in a Systems Level Language

Eric Holk, Milinda Pathirage, Arun Chauhan, Andrew Lumsdaine, Nicholas D. Matsakis

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Tags: Computer science, CUDA, High-level Languages, nVidia, nVidia GeForce GTX 460, OpenCL, Package, PTX

June 17, 2013 by hgpu

Real-space density functional theory on graphical processing units: computational approach and comparison to Gaussian basis set methods

Xavier Andrade, Alan Aspuru-Guzik

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Tags: ATI, ATI Radeon HD 7970, Computational Physics, Condensed matter, Data parallelism, nVidia, OpenCL, Package, Physics, Tesla K20

June 14, 2013 by hgpu

FastSpMM: An Efficient Library for Sparse Matrix Matrix Product on GPUs

Gloria Ortega, Francisco Vazquez, Inmaculada Garcia, Ester M. Garzon

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Tags: Computer science, Matrix multiplication, nVidia, nVidia GeForce GTX 480, Package, Sparse matrix, Tesla C2050

June 12, 2013 by hgpu

How a Single Chip Causes Massive Power Bills. GPUSimPow: A GPGPU Power Simulator

Jan Lucas, Sohan Lal, Michael Andersch, Mauricio Alvarez-Mesa, Ben Juurlink

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Tags: Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GT 240, nVidia GeForce GTX 580, Package

June 7, 2013 by hgpu

Composition and Reuse with Compiled Domain-Specific Languages

Arvind K. Sujeeth, Tiark Rompf, Kevin J. Brown, HyoukJoong Lee, Hassan Chafi, Victoria Popic, Michael Wu, Aleksander Prokopec, Vojin Jovanovic, Martin Odersky, Kunle Olukotun

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Tags: Computer science, CUDA, MPI, nVidia, Package, Performance, Tesla C2050

May 31, 2013 by hgpu

GPU Multiple Sequence Alignment Fourier-Space Cross-Correlation Alignment

Matthias Alexander Lee

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Tags: Bioinformatics, Biology, Computational Complexity, CUDA, nVidia, Package, Python, RNA, Sequence alignment, Signal processing

May 31, 2013 by hgpu

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

KernelBenchX: A Comprehensive Benchmark for Evaluating LLM-Generated GPU Kernels

CUDA Kernel Fusion Benchmarks

Analyzing the Impact of Kernel Fusion on GPU Tensor Operation Performance: A Systematic Performance Study

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

Agentic Code Optimization via Compiler-LLM Cooperation

MegaTrain: Full Precision Training of 100B+ Parameter Large Language Models on a Single GPU

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

Triton-Sanitizer: A Fast and Device-Agnostic Memory Sanitizer for Triton with Rich Diagnostic Context

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