high performance computing on graphics processing units: hgpu.org

Moritz Kreutzer, Jonas Thies, Melven Rohrig-Zollner, Andreas Pieper, Faisal Shahzad, Martin Galgon, Achim Basermann, Holger Fehske, Georg Hager, Gerhard Wellein

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Tags: Computer science, CUDA, Heterogeneous systems, Intel Xeon Phi, Linear Algebra, MPI, nVidia, OpenMP, Package, Sparse matrix, Tesla K20

July 30, 2015 by hgpu

Study of Sparse-Matrix Vector Multiplication (SpMV) on Different Architectures and Libraries

Naveen Anand Subramaniam, Omkar Deshmukh, Vennila Megavannan, Dan Negrut

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Tags: Computer science, CUDA, nVidia, nVidia GeForce GTX 770, OpenCL, Performance, Sparse matrix, Tesla K20, Tesla K40

June 19, 2015 by hgpu

Characterizing Dataset Dependence for Sparse Matrix-Vector Multiplication on GPUs

N. Sedaghati, A. Ashari, L. N. Pouchet, S. Parthasarathy, P. Sadayappan

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Tags: Computer science, CUDA, Linear Algebra, nVidia, Performance, Sparse matrix, Tesla K40

June 16, 2015 by hgpu

Type-safe Runtime Code Generation: Accelerate to LLVM

Trevor L. McDonell, Manuel M. T. Chakravarty, Vinod Grover, Ryan R. Newton

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Tags: Code generation, Computer science, CUDA, nVidia, Package, Sparse matrix, Tesla K40

June 14, 2015 by hgpu

Automatic Selection of Sparse Matrix Representation on GPUs

Naser Sedaghati, Te Mu, Louis-Noel Pouchet, Srinivasan Parthasarathy, P. Sadayappan

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Tags: Computer science, CUDA, Machine learning, nVidia, nVidia GeForce GTX 580, Physics, Sparse matrix, Tesla K20, Tesla K40

June 14, 2015 by hgpu

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

Profile Util library: A quick and easy way to get MPI, OpenMP and GPU runtime information

MaC: Mamba CPU performance predictor

MambaCPU: Enhanced Correlation Mining with State Space Models for CPU Performance Prediction

Mixed-precision finite element kernels

Mixed-precision finite element kernels and assembly: Rounding error analysis and hardware acceleration

SHMT: The Simultaneous and Heterogenous Multithreading project

General-Purpose Computing on Tensor Processors

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

Online Energy Optimization in GPUs: A Multi-Armed Bandit Approach

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high performance computing on graphics processing units: hgpu.org

Compositional Compilation for Sparse, Irregular Data Parallelism

Task Parallel Incomplete Cholesky Factorization using 2D Partitioned-Block Layout

Basker: A Threaded Sparse LU Factorization Utilizing Hierarchical Parallelism and Data Layouts

Efficient Sparse Matrix-Vector Multiplication on GPUs using the CSR Storage Format

Structural Agnostic SpMV: Adapting CSR-Adaptive for Irregular Matrices

Accelerating Applications with Pattern-specific Optimizations on Accelerators and Coprocessors

Three storage formats for sparse matrices on GPGPUs

GHOST: Building blocks for high performance sparse linear algebra on heterogeneous systems

Study of Sparse-Matrix Vector Multiplication (SpMV) on Different Architectures and Libraries

Characterizing Dataset Dependence for Sparse Matrix-Vector Multiplication on GPUs

Type-safe Runtime Code Generation: Accelerate to LLVM

Automatic Selection of Sparse Matrix Representation on GPUs

Recent source codes

Kokkidio: Kokkos+Eigen - write fast, readable multi-platform code

LLaMA-Inference-Bench

MATAR

Over-synchronization in GPU Programs

profile_util: simple C++ library to report memory, timing, thread affinity

MaC: Mamba CPU performance predictor

Mixed-precision finite element kernels

SHMT: The Simultaneous and Heterogenous Multithreading project

Superpipeline: A Universal Approach for Reducing GPU Memory Usage in Large Models

EnergyUCB-Bandit

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