high performance computing on graphics processing units: hgpu.org

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Accelerating Sparse Matrix Kernels on Graphics Processing Units

Kiran Kumar Matam

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Tags: Algorithms, Computer science, CUDA, Matrix multiplication, nVidia, Sparse matrix, Tesla C1060, Tesla C2050, Thesis

September 14, 2012 by hgpu

Performance Comparison Between Cg-based and CUDA-based Matrix Multiplications

Luke West, Jong Kwan Lee

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Tags: Cg, Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce GTX 285, nVidia GeForce GTX 480, Performance

August 28, 2012 by hgpu

Optimization Techniques for CUDA Application

Tushar Athawale, Xie Xu

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Tags: Computer science, CUDA, Matrix multiplication, nVidia, Optimization, Performance

August 28, 2012 by hgpu

Parallel programming on GPU using Intel Array Building Blocks

Chatura A. Ettigi

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Tags: Computer science, CUDA, Matrix multiplication, nVidia, nVidia GeForce GTX 260 M, Programming techniques, Thesis

July 31, 2012 by hgpu

Sparse Matrix Matrix Multiplication on Hybrid CPU+GPU Platforms

Kiran Kumar Matam, Siva Rama Krishna Bharadwaj, Kishore Kothapalli

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Tags: Computer science, CUDA, Matrix multiplication, nVidia, Optimization, Sparse matrix, Tesla C2050

July 16, 2012 by hgpu

Implementing a Code Generator for Fast Matrix Multiplication in OpenCL on the GPU

Kazuya Matsumoto, Naohito Nakasato, Stanislav G. Sedukhin

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Tags: ATI, ATI Radeon HD 7970, Code generation, Computer science, Matrix multiplication, OpenCL

July 15, 2012 by hgpu

Data Partitioning on Heterogeneous Multicore and Multi-GPU Systems Using Functional Performance Models of Data-Parallel Applications

Ziming Zhong, Vladimir Rychkov, Alexey Lastovetsky

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Tags: Algorithms, Computer science, CUDA, Heterogeneous systems, Matrix multiplication, nVidia, nVidia GeForce GTX 680, Tesla C870

July 12, 2012 by hgpu

An Optimized Large-Scale Hybrid DGEMM Design for CPUs and ATI GPUs

Jiajia Li, Xingjian Li, Guangming Tan, Mingyu Chen, Ninghui Sun

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Tags: Algorithms, ATI, ATI CAL, ATI Radeon HD 5970, ATI Stream, Computer science, Heterogeneous systems, Matrix multiplication, Optimization, Package

June 29, 2012 by hgpu

Software Performance Analysis with Parallel Programming Approaches

Suneeta H. Angadi, G. T. Raju, Abhishek B

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Tags: Computer science, Matrix multiplication, OpenCL, OpenMP, Optimization, Performance

June 27, 2012 by hgpu

Bacon: A GPU Programming System With Just in Time Specialization

Nat Tuck

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Tags: ATI, ATI Radeon HD 5830, Benchmarking, Computer science, Matrix multiplication, OpenCL

June 23, 2012 by hgpu

CUDAICA: GPU optimization of Infomax-ICA EEG analysis

Federico Raimondo, Juan Kamienkowski, Mariano Sigman, Diego Fernandez Slezak

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Tags: Algorithms, Biology, CUBLAS, CUDA, Linear Algebra, Matrix multiplication, Neuroscience, nVidia, nVidia GeForce GTX 560, Package, Tesla C2070

June 11, 2012 by hgpu

Hierarchical Partitioning Algorithm for Scientific Computing on Highly Heterogeneous CPU + GPU Clusters

David Clarke, Aleksandar Ilic, Alexey Lastovetsky, Leonel Sousa

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Tags: Computer science, CPU cluster, GPU cluster, Heterogeneous systems, Matrix multiplication, nVidia, Tesla C2050, Tesla T10

June 5, 2012 by hgpu

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

SimSYCL: A SYCL Implementation Targeting Development, Debugging, Simulation and Conformance

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

gpu_tracker: Python package for tracking and profiling GPU utilization in both desktop and high-performance computing environments

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

94% on CIFAR-10 in 3.29 Seconds on a Single GPU

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

LOOPer: A Learned Automatic Code Optimizer For Polyhedral Compilers

OpenMC Monte Carlo Code

Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs

Polygeist: C/C++ frontend for MLIR

Retargeting and Respecializing GPU Workloads for Performance Portability

Parallel Gaussian process with kernel approximation in CUDA

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high performance computing on graphics processing units: hgpu.org

Accelerating Sparse Matrix Kernels on Graphics Processing Units

Performance Comparison Between Cg-based and CUDA-based Matrix Multiplications

Optimization Techniques for CUDA Application

Parallel programming on GPU using Intel Array Building Blocks

Sparse Matrix Matrix Multiplication on Hybrid CPU+GPU Platforms

Implementing a Code Generator for Fast Matrix Multiplication in OpenCL on the GPU

Data Partitioning on Heterogeneous Multicore and Multi-GPU Systems Using Functional Performance Models of Data-Parallel Applications

An Optimized Large-Scale Hybrid DGEMM Design for CPUs and ATI GPUs

Software Performance Analysis with Parallel Programming Approaches

Bacon: A GPU Programming System With Just in Time Specialization

CUDAICA: GPU optimization of Infomax-ICA EEG analysis

Hierarchical Partitioning Algorithm for Scientific Computing on Highly Heterogeneous CPU + GPU Clusters

Recent source codes

SimSYCL: Synchronous, single-threaded, library-only SYCL implementation for debugging and verification

GPU plugin for PySCF

QArray

Celerity: High-level C++ for Accelerator Clusters

gpu_tracker: Context manager and CLI that tracks the computational-resource-usage of a code block or shell command, particularly the GPU usage

CIFAR-10 Airbench: 94% on CIFAR-10 in 3.29 second

LOOPer: a polyhedral compiler for expressing fast and portable data parallel algorithms

OpenMC Monte Carlo Code

Polygeist: C/C++ frontend for MLIR

Parallel Gaussian process with kernel approximation in CUDA

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