high performance computing on graphics processing units: hgpu.org

hgpu.org » Tesla K40

A novel approach to evaluating compact finite differences and similar tridiagonal schemes on GPU-accelerated clusters

Ashwin Trikuta Srinath

View

Tags: Algorithms, Computer science, CUDA, Finite difference, Fluid dynamics, GPU cluster, Numerical simulation, nVidia, OpenMPI, Tesla K20, Tesla K40, Thesis

February 3, 2016 by hgpu

A Reproducible Research Methodology for Designing and Conducting Faithful Simulations of Dynamic HPC Applications

Luka Stanisic

View

Download (PDF)

Tags: Computer science, CUDA, Heterogeneous systems, Linear Algebra, nVidia, nVidia Quadro FX 5800, Tesla C2050, Tesla K20, Tesla K40, Tesla M2070, Tesla M2075, Thesis

February 1, 2016 by hgpu

High-Performance Tensor Contractions for GPUs

A. Abdelfattah, M. Baboulin, V. Dobrev, J. Dongarra, C. Earl, J. Falcou, A. Haidar, I. Karlin, Tz. Kolev, I. Masliah, S. Tomov

View

Download (PDF)

Tags: Algorithms, Code generation, Computer science, CUDA, FEM, Finite element method, Linear Algebra, Matrix multiplication, nVidia, OpenMP, Tesla K40

January 29, 2016 by hgpu

GeNN: a code generation framework for accelerated brain simulations

Esin Yavuz, James Turner, Thomas Nowotny

View

Download (PDF)

Source codes

Tags: Biology, Code generation, CUDA, Neural networks, Neuroscience, Numerical simulation, nVidia, nVidia Quadro K 2000 M, Package, Tesla C2070, Tesla K40

January 29, 2016 by hgpu

Compositional Compilation for Sparse, Irregular Data Parallelism

Adam Harries, Michel Steuwer, Murray Cole, Alan Gray, Christophe Dubach

View

Download (PDF)

Tags: Computer science, Data parallelism, nVidia, OpenCL, Sparse matrix, Tesla K40

January 26, 2016 by hgpu

GPU Multisplit

Saman Ashkiani, Andrew Davidson, Ulrich Meyer, John D. Owens

View

Download (PDF)

Tags: Algorithms, Computer science, CUDA, Databases, nVidia, nVidia GeForce GTX 750 Ti, Tesla K40

January 22, 2016 by hgpu

Contributions of hybrid architectures to depth imaging: a CPU, APU and GPU comparative study

Issam Said

View

Download (PDF)

Tags: APU, Computer science, Geophysics, Geoscience, GPU cluster, MPI, nVidia, OpenCL, Tesla K40, Thesis

January 16, 2016 by hgpu

A Workload Balanced MapReduce Framework on GPU Platforms

Yue Zhang

View

Download (PDF)

Tags: Benchmarking, Computer science, CUDA, MapReduce, nVidia, Performance, Tesla K40, Thesis

January 12, 2016 by hgpu

Batched Linear Algebra Problems on GPU Accelerators

Tingxing Dong

View

Download (PDF)

Tags: Computer science, CUDA, Factorization, Heterogeneous systems, Linear Algebra, nVidia, Tesla K40, Thesis

January 4, 2016 by hgpu

Programming Models and Scheduling Techniques for Heterogeneous Architectures

Judit Planas Carbonell

View

Download (PDF)

Tags: Computer science, Heterogeneous systems, Intel Xeon Phi, nVidia, OmpSs, Task scheduling, Tesla K20, Tesla K40, Tesla M2090, Thesis

January 4, 2016 by hgpu

A Comparison of the performance of HPC Accelerators

Xiao Li

View

Download (PDF)

Tags: AMD FirePro W9100, ATI, Computer science, nVidia, OpenCL, Performance, Tesla K40, Thesis

December 31, 2015 by hgpu

On the Development and Implementation of High-Order Flux Reconstruction Schemes for Computational Fluid Dynamics

Freddie David Witherden

View

Download (PDF)

Source codes

Tags: AMD FirePro W9100, ATI, CUDA, Finite difference, Fluid dynamics, Heterogeneous systems, MPI, nVidia, OpenCL, Package, Python, Tesla K40, Thesis

December 23, 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

See all packages

* * *

high performance computing on graphics processing units: hgpu.org

A novel approach to evaluating compact finite differences and similar tridiagonal schemes on GPU-accelerated clusters

A Reproducible Research Methodology for Designing and Conducting Faithful Simulations of Dynamic HPC Applications

High-Performance Tensor Contractions for GPUs

GeNN: a code generation framework for accelerated brain simulations

Compositional Compilation for Sparse, Irregular Data Parallelism

GPU Multisplit

Contributions of hybrid architectures to depth imaging: a CPU, APU and GPU comparative study

A Workload Balanced MapReduce Framework on GPU Platforms

Batched Linear Algebra Problems on GPU Accelerators

Programming Models and Scheduling Techniques for Heterogeneous Architectures

A Comparison of the performance of HPC Accelerators

On the Development and Implementation of High-Order Flux Reconstruction Schemes for Computational Fluid Dynamics

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

Most viewed papers (last 30 days)