high performance computing on graphics processing units: hgpu.org

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Brute-Force k-Nearest Neighbors Search on the GPU

Shengren Li, Nina Amenta

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Tags: Computer science, CUDA, Matrix multiplication, Nearest neighbour, nVidia, nVidia GeForce GTX 460, Sorting

October 3, 2015 by hgpu

linalg: Matrix Computations in Apache Spark

Reza Bosagh Zadeh, Xiangrui Meng, Burak Yavuz, Aaron Staple, Li Pu, Shivaram Venkataraman, Evan Sparks, Alexander Ulanov, Matei Zaharia

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Tags: Algorithms, Benchmarking, Computer science, CUBLAS, CUDA, Linear Algebra, Machine learning, Matrix multiplication, nVidia, Package, Scala, Tesla M2050

September 15, 2015 by hgpu

Optimizing strassen matrix multiply on GPUs

Ayaz ul Hasan Khan, Mayez Al-Mouhamed, Allam Fatayer

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Data parallelism, Matrix multiplication, nVidia, Tesla K20

August 11, 2015 by hgpu

A Data-oriented Method for Scheduling Dependent Tasks on High-density Multi-GPU Systems

Peng Zhang, Yuxiang Gao, Meikang Qiu

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Tags: Algorithms, Computer science, CUDA, Matrix inversion, Matrix multiplication, nVidia, Task scheduling, Tesla K40

August 3, 2015 by hgpu

Irregular algorithms on the Xeon Phi

Sander Lijbrink

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Tags: Algorithms, Computer science, Intel Xeon Phi, Matrix multiplication, OpenMP, Thesis

July 27, 2015 by hgpu

Autotuning Tensor Contraction Computations on GPUs

Axel Rivera, Mary Hall, Paul D. Hovland, Elizabeth Jessup, Thomas Nelson, Boyana Norris

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Tags: Benchmarking, Code generation, Computer science, CUDA, Matrix multiplication, nVidia, OpenACC, Tesla C2050, Tesla K20

June 19, 2015 by hgpu

A Framework for General Sparse Matrix-Matrix Multiplication on GPUs and Heterogeneous Processors

Weifeng Liu, Brian Vinter

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Tags: AMD Radeon R9 290X, ATI, Computer science, CUDA, Mathematical Software, Matrix multiplication, Numerical Analysis, nVidia, nVidia GeForce GTX 980, nVidia GeForce GTX Titan, OpenCL, Package, Path problems, Sparse matrix

April 23, 2015 by hgpu

Optimizing ASP.NET with C++ AMP on the GPU

Scott Zimmerman

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Tags: C++ AMP, Cloud, Computer science, Matrix multiplication, nVidia, nVidia GRID K520, Package

April 17, 2015 by hgpu

PTX2Kernel: Converting PTX Code into Compilable Kernels

Qiankun Dong, Tao Li, Shuai Zhang, Xiaofan Jiao, Jiabing Leng

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Tags: Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce GTX 480, PTX, Tesla K40

March 22, 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

Fast Radix Sort for Sparse Linear Algebra on GPU

Lukas Polok, Viorela Ila, Pavel Smrz

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Tags: Algorithms, Computer science, CUDA, Factorization, Linear Algebra, Matrix multiplication, nVidia, nVidia GeForce GTX 680, nVidia GeForce GTX 780, OpenCL, Sorting, Sparse matrix

March 18, 2015 by hgpu

Exploiting Concurrency Patterns with Heterogeneous Task and Data Parallelism

A Geetha Venkatesh

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Tags: Computer science, Data parallelism, Heterogeneous systems, Matrix multiplication, nVidia, OpenCL, Tesla C2070, Thesis

February 3, 2015 by hgpu

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

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high performance computing on graphics processing units: hgpu.org

Brute-Force k-Nearest Neighbors Search on the GPU

linalg: Matrix Computations in Apache Spark

Optimizing strassen matrix multiply on GPUs

A Data-oriented Method for Scheduling Dependent Tasks on High-density Multi-GPU Systems

Irregular algorithms on the Xeon Phi

Autotuning Tensor Contraction Computations on GPUs

A Framework for General Sparse Matrix-Matrix Multiplication on GPUs and Heterogeneous Processors

PTX2Kernel: Converting PTX Code into Compilable Kernels

Fast Sparse Matrix Multiplication on GPU

Fast Radix Sort for Sparse Linear Algebra on GPU

Exploiting Concurrency Patterns with Heterogeneous Task and Data Parallelism

Recent source codes

ParaCodex: A Profiling-Guided Autonomous Coding Agent for Reliable Parallel Code Generation and Translation

SeedFold: Scaling Biomolecular Structure Prediction

Tilus: A Tile-Level GPU Kernel Programming Language

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

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