high performance computing on graphics processing units: hgpu.org

hgpu.org » Energy-efficient computing

A Hierarchical Thread Scheduler and Register File for Energy-efficient Throughput Processors

Mark Gebhart, Daniel R. Johnson, David Tarjan, Stephen W. Keckler, William J. Dally, Erik Lindholm, Kevin Skadron

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Tags: Computer science, CUDA, Energy-efficient computing, nVidia

February 22, 2012 by hgpu

An Energy-Efficient Heterogeneous System for Embedded Learning and Classification

Abhinandan Majumdar, Srihari Cadambi, Srimat T. Chakradhar

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Tags: Computer science, Energy-efficient computing, FPGA, Heterogeneous systems, nVidia, nVidia ION, Tesla C1070

January 20, 2012 by hgpu

Power Profiling and Optimization for Heterogeneous Multi-Core Systems

Kuen Hung Tsoi, Wayne Luk

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Tags: Computer science, CUDA, Design space exploration, Energy-efficient computing, FPGA, Heterogeneous systems, N-body simulation, nVidia, Optimization, Tesla C1060

January 19, 2012 by hgpu

A Compile-Time Managed Multi-Level Register File Hierarchy

Mark Gebhart, Stephen W. Keckler, William J. Dally

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Tags: Algorithms, Computer science, CUDA, Energy-efficient computing, nVidia, Optimization, PTX

January 19, 2012 by hgpu

Optimization of power consumption in the iterative solution of sparse linear systems on graphics processors

Hartwig Anzt, Maribel Castillo, Juan C. Fernandez, Vincent Heuveline, Francisco D. Igual, Rafael Mayo and Enrique S. Quintana-Orti

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Tags: Algorithms, Computer science, CUDA, Energy-efficient computing, Mixed precision, nVidia, Tesla C1060

January 11, 2012 by hgpu

Analysis and optimization of power consumption in the iterative solution of sparse linear systems on multi-core and many-core platforms

Hartwig Anzt, Vincent Heuveline, Jose I. Aliaga, Maribel Castillo, Juan C. Fernandez, Rafael Mayo, Enrique S. Quintana-Orti

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Tags: Algorithms, Computer science, CUDA, Energy-efficient computing, nVidia, Optimization, Sparse linear iterative solvers, Tesla C1060

January 11, 2012 by hgpu

Power-performance comparison of single-task driven many-cores

Fuat Keceli, Tali Moreshet, Uzi Vishkin

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Tags: Algorithms, Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 280

January 11, 2012 by hgpu

Low-power Task Scheduling for GPU Energy Reduction

Li Tang, Yiji Zhang

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Tags: Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 210, nVidia GeForce GTX 280, nVidia GeForce GTX 570, Operating systems, Task scheduling

January 6, 2012 by hgpu

Power consumption of mixed precision in the iterative solution of sparse linear systems

Hartwig Anzt, Vincent Heuveline, Bjorn Rocker, Maribel Castillo, Juan C. Fernandez, Rafael Mayo, Enrique S. Quintana-Orti

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Tags: Algorithms, Computer science, CUDA, Energy-efficient computing, Mixed precision, nVidia, Tesla C1060

December 14, 2011 by hgpu

rCUDA: Reducing the number of GPU-based accelerators in high performance clusters

J. Duato, Pena, A.J. a, F. Silla, R. Mayo, E.S. Quintana-Orti

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Tags: Computer science, CUDA, Distributed computing, Energy-efficient computing, nVidia, Package, PC cluster, Tesla C1060

November 12, 2011 by hgpu

Sustainable GPU Computing at Scale

Justin Y. Shi, Moussa Taifi, Abdallah Khreishah, Jie Wu

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Tags: Computer science, CUBLAS, CUDA, Energy-efficient computing, Fault tolerance, nVidia, OpenMPI, Tesla S1070

November 12, 2011 by hgpu

Exploiting Heterogeneity for Energy Efficiency in Chip Multiprocessors

V. Saripalli, G. Sun, A. Mishra, Y. Xie, S. Datta, V. Narayanan

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Tags: Computer science, Energy-efficient computing, Heterogeneous systems

November 12, 2011 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

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high performance computing on graphics processing units: hgpu.org

A Hierarchical Thread Scheduler and Register File for Energy-efficient Throughput Processors

An Energy-Efficient Heterogeneous System for Embedded Learning and Classification

Power Profiling and Optimization for Heterogeneous Multi-Core Systems

A Compile-Time Managed Multi-Level Register File Hierarchy

Optimization of power consumption in the iterative solution of sparse linear systems on graphics processors

Analysis and optimization of power consumption in the iterative solution of sparse linear systems on multi-core and many-core platforms

Power-performance comparison of single-task driven many-cores

Low-power Task Scheduling for GPU Energy Reduction

Power consumption of mixed precision in the iterative solution of sparse linear systems

rCUDA: Reducing the number of GPU-based accelerators in high performance clusters

Sustainable GPU Computing at Scale

Exploiting Heterogeneity for Energy Efficiency in Chip Multiprocessors

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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