high performance computing on graphics processing units: hgpu.org

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Autotuning, Code Generation and Optimizing Compiler Technology for GPUs

Malik Muhammad Zaki Murtaza Khan

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Tags: Code generation, Computer science, CUBLAS, CUDA, Linear Algebra, nVidia, Tesla C2050, Thesis

January 26, 2013 by hgpu

Numerical computations in Java with CUDA

Bogdan Oancea, Tudorel Andrei, Andreea Iluzia Iacob

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Tags: Computer science, CUBLAS, CUDA, Java, Linear Algebra, nVidia, nVidia GeForce 9500 GT

January 7, 2013 by hgpu

Task-based Conjugate-Gradient for multi-GPUs platforms

Emmanuel Agullo, Luc Giraud, Abdou Guermouche, Stojce Nakov, Jean Roman

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Tags: Algorithms, Computer science, Conjugate gradient solver, CUBLAS, CUDA, Linear Algebra, nVidia, Tesla C2070

December 25, 2012 by hgpu

Alternating Maximization: Unifying Framework for 8 Sparse PCA Formulations and Efficient Parallel Codes

Peter Richtarik, Martin Takac, Selin Damla Ahipasaoglu

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Tags: CUBLAS, CUDA, Machine learning, nVidia, Package, Statistics

December 18, 2012 by hgpu

Matrix-Matrix Multiplications on GPUs for Accelerating a Parallel Fluid Dynamics Code

Kenneth Webster

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Tags: CUBLAS, CUDA, Fluid dynamics, Matrix multiplication, nVidia, Tesla M2070, Thesis

December 12, 2012 by hgpu

Implementing QR Factorization Updating Algorithms on GPUs

Robert Andrew, Nicholas Dingle

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Factorization, Linear Algebra, Numerical Analysis, nVidia, Tesla M2050

December 10, 2012 by hgpu

Use of CUDA for the Continuous Space Language Model

Elizabeth A. Thompson, Timothy Anderson

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Tags: CUBLAS, CUDA, nVidia, nVidia Quadro FX 2700 M, Signal processing

November 16, 2012 by hgpu

Adapting Irregular Computations to Large CPU-GPU Clusters in the MADNESS Framework

Vlad Slavici, Raghu Varier, Gene Cooperman, Robert J. Harrison

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Tags: Chemistry, Computational chemistry, Computer science, CUBLAS, CUDA, GPU cluster, nVidia, Tesla M2090

November 6, 2012 by hgpu

Forecasting high frequency financial time series using parallel FFN with CUDA and ZeroMQ

Paola Arce, Cristian Maureira, Roberto Bonvallet, Cesar Fernandez

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Tags: Algorithms, CUBLAS, CUDA, Finance, Linear Algebra, Machine learning, Neural networks, nVidia, Tesla M2050

September 20, 2012 by hgpu

Systematic Approach in Optimizing Numerical Memory-Bound Kernels on GPU

Ahmad Abdelfattah, David Keyes, Hatem Ltaief

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Tags: Algorithms, Computer science, CUBLAS, CUDA, Linear Algebra, nVidia, Tesla C2070

September 13, 2012 by hgpu

GPU-accelerated WZ Factorization with the Use of the CUBLAS Library

Beata Bylina, Jaroslaw Bylina

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Tags: Computer science, CUBLAS, CUDA, Factorization, nVidia, Tesla C2050

September 3, 2012 by hgpu

A Unified Optimizing Compiler Framework for Different GPGPU Architectures

Yi Yang, Ping Xiang, Jingfei Kong, Mike Mantor, Huiyang Zhou

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Tags: ATI, ATI Radeon HD 5870, Compilers, Computer science, CUBLAS, CUDA, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480, OpenCL, Optimization, Package, Programming Languages

August 27, 2012 by hgpu

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Accelerating a Linear Programming Algorithm on AMD GPUs

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Towards Efficient and Practical GPU Multitasking in the Era of LLM

Bandicoot: C++ library for GPU accelerated linear algebra

Bandicoot: A Templated C++ Library for GPU Linear Algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

The Fused Kernel Library: A C++ API to Develop Highly-Efficient GPU Libraries

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

Block: Balancing Load in LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

SIGMo: High-Throughput Batched Subgraph Isomorphism on GPUs for Molecular Matching

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

DGEMM without FP64 Arithmetic – using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

See all packages

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high performance computing on graphics processing units: hgpu.org

Autotuning, Code Generation and Optimizing Compiler Technology for GPUs

Numerical computations in Java with CUDA

Task-based Conjugate-Gradient for multi-GPUs platforms

Alternating Maximization: Unifying Framework for 8 Sparse PCA Formulations and Efficient Parallel Codes

Matrix-Matrix Multiplications on GPUs for Accelerating a Parallel Fluid Dynamics Code

Implementing QR Factorization Updating Algorithms on GPUs

Use of CUDA for the Continuous Space Language Model

Adapting Irregular Computations to Large CPU-GPU Clusters in the MADNESS Framework

Forecasting high frequency financial time series using parallel FFN with CUDA and ZeroMQ

Systematic Approach in Optimizing Numerical Memory-Bound Kernels on GPU

GPU-accelerated WZ Factorization with the Use of the CUBLAS Library

A Unified Optimizing Compiler Framework for Different GPGPU Architectures

Recent source codes

torchPDLP: Primal-Dual Linear Programming in PyTorch. In collaboration with AMD and IPAM

Benchmarks for Dissecting CPU-GPU Unified Physical Memory on AMD MI300A APUs

kvcached: Elastic KV cache for dynamic GPU sharing and efficient multi-LLM inference

Bandicoot: C++ library for GPU accelerated linear algebra

Luthier: Bridging Auto-Tuning and Vendor Libraries for Efficient Deep Learning Inference

Fused Kernel Library (FKL)

GPUHammer: Rowhammer Attacks on GPU Memories are Practical

Block: Balance Loader of LLM Serving with Context, Knowledge and Predictive Scheduling

SIGMo: Scalable Isomorphism Graph Matching on GPUs

DGEMM without FP64 Arithmetic - using FP64 Emulation and FP8 Tensor Cores with Ozaki Scheme

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