Software Power Analysis And Optimization For Power-Aware Multicore Systems

Among all the factors in sustainable computing, power dissipation and energy consumption, arguably speaking, are fundamental aspects of modern computer systems. Different from performance metric, power dissipation is not easy to measure because hardware instrumentation is usually required. Yet as an indispensable component of a computer system, software becomes a major factor affecting power dissipation besides hardware energy-efficiency and power states. With detailed information on resource usage and power dissipation of an application/software, software developers will be able to leverage algorithms and implementations in order to produce power-efficient solutions. Hardware instrumentation, despite its accuracy, is costly and complicated to set up. A general solution to connect software with hardware along with detailed power and system information will improve the system overall efficiency. In this work, we design and implement a general solution to analyze and model software power dissipation. Based on the analysis, we propose a combined solution to optimize the energy efficiency of parallel workload. Starting from the hands-on power measurement method in detail, we provide a fine-grain power profile of two computer systems using hardware instrumentation. Being focusing on dynamic power dissipation analysis, we propose a two-level power model for power-aware multicore computer systems. Based on the model, we design and implement SPAN to relate power dissipation to the different portions of an application using the proposed power model. By using SPAN, developers can easily identify the sections of code consuming the most power in the program. Alternatively, to enable automatic source code instrumentation, we utilize compiler techniques to insert profiling code before and after each function in source code. The expected outcome includes an open source function level power profiling tool, Safari. Using the profiling tools, we propose a model to capture the relationship between concurrency (C), power (P) and execution time (T). By changing the system configuration for different parallel workload, we are able to achieve optimal/near optimal energy-efficient execution of a given workload on a specific platform

Adaptive Transactional Memories: Performance and Energy Consumption Tradeoffs

Energy efficiency is becoming a pressing issue, especially in large data centers where it entails, at the same time, a non-negligible management cost, an enhancement of hardware fault probability, and a significant environmental footprint. In this paper, we study how Software Transactional Memories (STM) can provide benefits on both power saving and the overall applications’ execution performance. This is related to the fact that encapsulating shared-data accesses within transactions gives the freedom to the STM middleware to both ensure consistency and reduce the actual data contention, the latter having been shown to affect the overall power needed to complete the application’s execution. We have selected a set of self-adaptive extensions to existing STM middlewares (namely, TinySTM and R-STM) to prove how self-adapting computation can capture the actual degree of parallelism and/or logical contention on shared data in a better way, enhancing even more the intrinsic benefits provided by STM. Of course, this benefit comes at a cost, which is the actual execution time required by the proposed approaches to precisely tune the execution parameters for reducing power consumption and enhancing execution performance. Nevertheless, the results hereby provided show that adaptivity is a strictly necessary requirement to reduce energy consumption in STM systems: Without it, it is not possible to reach any acceptable level of energy efficiency at all

Exploring power behaviors and trade-offs of in-situ data analytics

pre-printAs scientific applications target exascale, challenges related to data and energy are becoming dominating concerns. For example, coupled simulation workflows are increasingly adopting in-situ data processing and analysis techniques to address costs and overheads due to data movement and I/O. However it is also critical to understand these overheads and associated trade-offs from an energy perspective. The goal of this paper is exploring data-related energy/performance trade-offs for end-to-end simulation workflows running at scale on current high-end computing systems. Specifically, this paper presents: (1) an analysis of the data-related behaviors of a combustion simulation workflow with an in-situ data analytics pipeline, running on the Titan system at ORNL; (2) a power model based on system power and data exchange patterns, which is empirically validated; and (3) the use of the model to characterize the energy behavior of the workflow and to explore energy/performance tradeoffs on current as well as emerging systems

Resource provisioning in Science Clouds: Requirements and challenges

Cloud computing has permeated into the information technology industry in the last few years, and it is emerging nowadays in scientific environments. Science user communities are demanding a broad range of computing power to satisfy the needs of high-performance applications, such as local clusters, high-performance computing systems, and computing grids. Different workloads are needed from different computational models, and the cloud is already considered as a promising paradigm. The scheduling and allocation of resources is always a challenging matter in any form of computation and clouds are not an exception. Science applications have unique features that differentiate their workloads, hence, their requirements have to be taken into consideration to be fulfilled when building a Science Cloud. This paper will discuss what are the main scheduling and resource allocation challenges for any Infrastructure as a Service provider supporting scientific applications