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Power Management Techniques for Data Centers: A Survey

Sparsh Mittal

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Tags: Architecture, big data, cpu, Energy-efficient computing, HPC, Power-efficient computing

February 8, 2015 by sparsh0mittal

A Survey of Power Management Techniques for Phase Change Memory

Sparsh Mittal

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Tags: cpu, Energy-efficient computing, GPU, Memory, non-volatile memory, Power-efficient computing

February 3, 2015 by sparsh0mittal

Opportunities for Nonvolatile Memory Systems in Extreme-Scale High Performance Computing

Jeffrey S. Vetter, Sparsh Mittal

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Tags: cpu, Energy-efficient computing, GPU, high performance computing, Memory, Storage system

January 21, 2015 by sparsh0mittal

A Survey of Architectural Techniques For Improving Cache Power Efficiency

Sparsh Mittal

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Tags: Architecture, cache, cpu, Energy-efficient computing, GPU, Intel, Memory, Power-efficient computing

January 19, 2015 by sparsh0mittal

A Survey Of Architectural Approaches for Managing Embedded DRAM and Non-volatile On-chip Caches

Sparsh Mittal; Jeffrey S. Vetter; Dong Li

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Tags: cache, Compiler, cpu, Energy-efficient computing, GPU, Hardware Architecture, Memory model

January 12, 2015 by sparsh0mittal

Performance and Power Comparisons Between Nvidia and ATI GPUs

Ying Zhang, Lu Peng, Bin Li, Jih-Kwon Peir, Jianmin Chen

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Tags: ATI, ATI Radeon HD 5870, Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 580, OpenCL, Performance

January 8, 2015 by hgpu

A Survey of Techniques For Improving Energy Efficiency in Embedded Computing Systems

Sparsh Mittal

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Tags: Embedded high-performance computing, Energy-efficient computing, FPGA, GPU, Power-efficient computing

January 7, 2015 by sparsh0mittal

GPU: Power vs Performance

Siddhartha Sankar Mondal

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Tags: Benchmarking, Computer science, CUDA, Energy-efficient computing, nVidia, nVidia GeForce GTX 580, Thesis

January 5, 2015 by hgpu

Bounds on the Energy Consumption of Computational Kernels

Andrew Gearhart

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Tags: Computer science, CUDA, Energy-efficient computing, Heterogeneous systems, nVidia, Package, Tesla K20, Thesis

November 3, 2014 by hgpu

Energy Evaluation for Applications with Different Thread Affinities on the Intel Xeon Phi

Gary Lawson, Masha Sosonkina, Yuzhong Shen

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Tags: Computer science, Energy-efficient computing, Intel Xeon Phi, Performance

August 15, 2014 by hgpu

Optimizing performance per watt on GPUs in High Performance Computing: temperature, frequency and voltage effects

D. C. Price, M. A. Clark, B. R. Barsdell, R. Babich, L. J. Greenhill

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Tags: Astrophysics, CUDA, Energy-efficient computing, Instrumentation and Methods for Astrophysics, nVidia, nVidia GeForce GTX 780, Package, Performance, Signal processing, Tesla K20

August 1, 2014 by hgpu

Energy-Efficient Collective Reduce and Allreduce Operations on Distributed GPUs

Lena Oden, Benjamin Klenk, Holger Froning

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Tags: Computer science, CUDA, Distributed computing, Energy-efficient computing, Heterogeneous systems, nVidia, Tesla K20

July 17, 2014 by hgpu

Tilus: A Tile-Level GPU Kernel Programming Language

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

Accurate Models of NVIDIA Tensor Cores

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

TritonForge: Profiling-Guided Framework for Automated Triton Kernel Optimization

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

tritonBLAS: Triton-based Analytical Approach for GEMM Kernel Parameter Selection

hls4ml: Machine learning on FPGAs using HLS

hls4ml: A Flexible, Open-Source Platform for Deep Learning Acceleration on Reconfigurable Hardware

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high performance computing on graphics processing units: hgpu.org

Power Management Techniques for Data Centers: A Survey

A Survey of Power Management Techniques for Phase Change Memory

Opportunities for Nonvolatile Memory Systems in Extreme-Scale High Performance Computing

A Survey of Architectural Techniques For Improving Cache Power Efficiency

A Survey Of Architectural Approaches for Managing Embedded DRAM and Non-volatile On-chip Caches

Performance and Power Comparisons Between Nvidia and ATI GPUs

A Survey of Techniques For Improving Energy Efficiency in Embedded Computing Systems

GPU: Power vs Performance

Bounds on the Energy Consumption of Computational Kernels

Energy Evaluation for Applications with Different Thread Affinities on the Intel Xeon Phi

Optimizing performance per watt on GPUs in High Performance Computing: temperature, frequency and voltage effects

Energy-Efficient Collective Reduce and Allreduce Operations on Distributed GPUs

Recent source codes

Tilus: A Tile-Level GPU Kernel Programming Language

Memory-Efficient Acceleration of Block Low-Rank Foundation Models on Resource Constrained GPUs

BoltzGen:Toward Universal Binder Design

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

cuPilot: A Strategy-Coordinated Multi-agent Framework for CUDA Kernel Evolution

MATLAB Tensor Core models

TritonForge: Transform PyTorch Operations into Optimized GPU Kernels with LLMs

RLTune: Hybrid Learning and Optimization-Based Dynamic Scheduling for DL Workloads on Heterogeneous GPU Clusters

tritonBLAS: A Lightweight Triton-based General Matrix Multiplication (GEMM) Library

hls4ml: Machine learning on FPGAs using HLS

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