high performance computing on graphics processing units: hgpu.org

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BLASX: A High Performance Level-3 BLAS Library for Heterogeneous Multi-GPU Computing

Linnan Wang, Wei Wu, Jianxiong Xiao, Yi Yang

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Tags: Benchmarking, Computer science, CUBLAS, CUDA, Heterogeneous systems, Linear Algebra, nVidia, nVidia GeForce GTX Titan X, Package, Performance, Tesla K40

October 22, 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

Framework for Batched and GPU-resident Factorization Algorithms Applied to Block Householder Transformations

Azzam Haidar, Tingxing "Tim" Dong, Stanimire Tomov, Piotr Luszczek, Jack Dongarra

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Factorization, nVidia, Tesla K40

April 12, 2015 by hgpu

Batched Matrix Computations on Hardware Accelerators

Azzam Haidar, Tingxing Dong, Piotr Luszczek, Stanimire Tomov, Jack Dongarra

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Factorization, Linear Algebra, nVidia, Tesla K40

April 12, 2015 by hgpu

Data driven scheduling approach for the multi-node multi-GPU Cholesky decomposition

Yuki Tsujita, Toshio Endo

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Tags: Computer science, CUBLAS, CUDA, Heterogeneous systems, Linear Algebra, nVidia, Task scheduling, Tesla K20

March 25, 2015 by hgpu

Parallel Implementation of the Finite Element Method on Graphics Processors for the Solution of Incompressible Flows

Mahmut Murat Gocmen

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Tags: Algorithms, CUBLAS, CUDA, FEM, Finite element method, Fluid dynamics, Lattice Boltzmann model, nVidia, nVidia GeForce GTX 280, Package, Tesla C2075, Thesis

January 16, 2015 by hgpu

Batched Matrix Computations on Hardware Accelerators Based on GPUs

Azzam Haidar, Tingxing Dong, Piotr Luszczek, Stanimire Tomov, Jack Dongarra

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Factorization, nVidia, Tesla K40

January 15, 2015 by hgpu

Minerva: A Scalable and Highly Efficient Training Platform for Deep Learning

Minjie Wang, Tianjun Xiao, Jianpeng Li, Jiaxing Zhang, Chuntao Hong, Zheng Zhang

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Tags: Computer science, CUBLAS, CUDA, Machine learning, Neural networks, nVidia

December 15, 2014 by hgpu

KBLAS: An Optimized Library for Dense Matrix-Vector Multiplication on GPU Accelerators

Ahmad Abdelfattah, David Keyes, Hatem Ltaief

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Tags: Computer science, CUBLAS, CUDA, Linear Algebra, Mathematical Software, nVidia, Package, Tesla K20

October 8, 2014 by hgpu

On the evaluation of matrix polynomials using several GPGPUs

Pedro Alonso, Murilo Boratto, J. Peinado, J. Ibanez, Jorge Sastre

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Heterogeneous systems, nVidia, Tesla K20, Thesis

September 20, 2014 by hgpu

Accelerating Krylov Subspace Solvers on Graphics Processing Units

Hartwig Anzt, Stanimire Tomov, Piotr Luszczek, Ichitaro Yamazaki, Jack Dongarra, William Sawyer

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Tags: Computer science, CUBLAS, CUDA, Linear Algebra, nVidia, Sparse matrix, Tesla K20

August 3, 2014 by hgpu

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

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

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

Agentic Code Optimization via Compiler-LLM Cooperation

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

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

LLMQ: Efficient Lower-Precision LLM Training for Consumer GPUs

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high performance computing on graphics processing units: hgpu.org

BLASX: A High Performance Level-3 BLAS Library for Heterogeneous Multi-GPU Computing

linalg: Matrix Computations in Apache Spark

Optimizing strassen matrix multiply on GPUs

Framework for Batched and GPU-resident Factorization Algorithms Applied to Block Householder Transformations

Batched Matrix Computations on Hardware Accelerators

Data driven scheduling approach for the multi-node multi-GPU Cholesky decomposition

Parallel Implementation of the Finite Element Method on Graphics Processors for the Solution of Incompressible Flows

Batched Matrix Computations on Hardware Accelerators Based on GPUs

Minerva: A Scalable and Highly Efficient Training Platform for Deep Learning

KBLAS: An Optimized Library for Dense Matrix-Vector Multiplication on GPU Accelerators

On the evaluation of matrix polynomials using several GPGPUs

Accelerating Krylov Subspace Solvers on Graphics Processing Units

Recent source codes

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

CUDA Kernel Fusion Benchmarks

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

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

Device Virtual Machine (DVM)

Agentic Code Optimization via Compiler-LLM Cooperation

AutoKernel: Autoresearch for GPU kernels. Give it any PyTorch model, go to sleep, wake up to optimized Triton kernels

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

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