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  1. Proceedings of Workshop on General Purpose Processing Using GPUs (GPGPU-7)
  2. Power Modeling for Heterogeneous Processors
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Application-aware Memory System for Fair and Efficient Execution of Concurrent GPGPU Applications
KMA: A Dynamic Memory Manager for OpenCL
Efficient Instrumentation of GPGPU Applications Using Information Flow Analysis and Symbolic Execution
Measuring GPU Power with the K20 Built-in Sensor
Performance Evaluation and Optimization Mechanisms for Inter-operable Graphics and Computation on GPUs
GLZSS: LZSS Lossless Data Compression Can Be Faster
ad-heap: an Efficient Heap Data Structure for Asymmetric Multicore Processors
A CPU: GPU Hybrid Implementation and Model-Driven Scheduling of the Fast Multipole Method
ParallelJS: An Execution Framework for JavaScript on Heterogeneous Systems
APR: A Novel Parallel Repacking Algorithm for Efficient GPGPU Parallel Code Transformation
Power Modeling for Heterogeneous Processors
Exploiting GPU Hardware Saturation for Fast Compiler Optimization

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Power Modeling for Heterogeneous Processors

Content Provider ACM Digital Library
Author Diop, Tahir Anderson, Jason Jerger, Natalie Enright
Abstract As power becomes an ever more important design consideration, there is a need for accurate power models at all stages of the design process. While power models are available for CPUs and GPUs, only simple models are available for heterogeneous processors. We present a micro-benchmark-based modeling technique that can be used for chip multiprocessor (CMPs) and accelerated processing units (APUs). We use our approach to model power on an Intel Xeon CPU and an AMD Fusion heterogeneous processor. The resulting error rate for the Xeon's model is below 3% and is only 7% for the Fusion. We also present a method to reduce the number of benchmarks required to create these models. Instead of running micro-benchmarks for every combination of factors (e.g. different operations or memory access patterns), we cluster similar micro-benchmarks to avoid unnecessary simulations. We show that it is possible to eliminate as many as 93% of the compute micro-benchmarks, while still producing power models having less than 10% error rate.
Starting Page 90
Ending Page 98
Page Count 9
File Format PDF
ISBN 9781450327664
DOI 10.1145/2576779.2576790
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2014-03-01
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Statistical analysis Benchmarking Power modeling
Content Type Text
Resource Type Article
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