Power Aware Computing on GPUs

Energy and power density concerns in modern processors have led to significant computer architecture research efforts in power-aware and temperature-aware computing. With power dissipation becoming an increasingly vexing problem, power analysis of Graphical Processing Unit (GPU) and its components has become crucial for hardware and software system design. Here, we describe our technique for a coordinated measurement approach that combines real total power measurement and per-component power estimation. To identify power consumption accurately, we introduce the Activity-based Model for GPUs (AMG), from which we identify activity factors and power for microarchitectures on GPUs that will help in analyzing power tradeoffs of one component versus another using microbenchmarks. The key challenge addressed in this thesis is real-time power consumption, which can be accurately estimated using NVIDIA\u27s Management Library (NVML) through Pthreads. We validated our model using Kill-A-Watt power meter and the results are accurate within 10\%. The resulting Performance Application Programming Interface (PAPI) NVML component offers real-time total power measurements for GPUs. This thesis also compares a single NVIDIA C2075 GPU running MAGMA (Matrix Algebra on GPU and Multicore Architectures) kernels, to a 48 core AMD Istanbul CPU running LAPACK

A Survey of Prediction and Classification Techniques in Multicore Processor Systems

In multicore processor systems, being able to accurately predict the future provides new optimization opportunities, which otherwise could not be exploited. For example, an oracle able to predict a certain application\u27s behavior running on a smart phone could direct the power manager to switch to appropriate dynamic voltage and frequency scaling modes that would guarantee minimum levels of desired performance while saving energy consumption and thereby prolonging battery life. Using predictions enables systems to become proactive rather than continue to operate in a reactive manner. This prediction-based proactive approach has become increasingly popular in the design and optimization of integrated circuits and of multicore processor systems. Prediction transforms from simple forecasting to sophisticated machine learning based prediction and classification that learns from existing data, employs data mining, and predicts future behavior. This can be exploited by novel optimization techniques that can span across all layers of the computing stack. In this survey paper, we present a discussion of the most popular techniques on prediction and classification in the general context of computing systems with emphasis on multicore processors. The paper is far from comprehensive, but, it will help the reader interested in employing prediction in optimization of multicore processor systems