Author manuscript, published in "WEED 2010- Workshop on Energy-Efficient Design (2010)" The Search for Energy-Efficient Building Blocks for the Data Center

This paper conducts a survey of several small clusters of machines in search of the most energy-efficient data center building block targeting data-intensive computing. We first evaluate the performance and power of single machines from the embedded, mobile, desktop, and server spaces. From this group, we narrow our choices to three system types. We build fivenode homogeneous clusters of each type and run Dryad, a distributed execution engine, with a collection of dataintensive workloads to measure the energy consumption per task on each cluster. For this collection of dataintensive workloads, our high-end mobile-class system was, on average, 80 % more energy-efficient than a cluster with embedded processors and at least 300% more energy-efficient than a cluster with low-power server processors.

Full-system power analysis and modeling for server environments

Abstract — The increasing costs of power delivery and cooling, as well as the trend toward higher-density computer systems, have created a growing demand for better power management in server environments. Despite the increasing interest in this issue, little work has been done in quantitatively understanding power consumption trends and developing simple yet accurate models to predict full-system power. We study the component-level power breakdown and variation, as well as temporal workload-specific power consumption of an instrumented power-optimized blade server. Using this analysis, we examine the validity of prior adhoc approaches to understanding power breakdown and quantify several interesting trends important for power modeling and management in the future. We also introduce Mantis, a nonintrusive method for modeling full-system power consumption and providing real-time power prediction. Mantis uses a onetime calibration phase to generate a model by correlating AC power measurements with user-level system utilization metrics. We experimentally validate the model on two server systems with drastically different power footprints and characteristics (a low-end blade and high-end compute-optimized server) using a variety of workloads. Mantis provides power estimates with high accuracy for both overall and temporal power consumption, making it a valuable tool for power-aware scheduling and analysis. I

JouleSort: A Balanced Energy-Efficiency Benchmark

The energy efficiency of computer systems is an important concern in a variety of contexts. In data centers, reducing energy use improves operating cost, scalability, reliability, and other factors. For mobile devices, energy consumption directly affects functionality and usability. We propose and motivate JouleSort, an external sort benchmark, for evaluating the energy efficiency of a wide range of computer systems from clusters to handhelds. We list the criteria, challenges, and pitfalls from our experience in creating a fair energyefficiency benchmark. Using a commercial sort, we demonstrate a JouleSort system that is over 3.5x as energy-efficient as last year’s estimated winner. This system is quite different from those currently used in data centers. It consists of a commodity mobile CPU and 13 laptop drives, connected by server-style I/O interfaces

Vector Lane Threading

Multi-lane vector processors achieve excellent computational throughput for programs with high data-level parallelism (DLP). However, application phases without significant DLP are unable to fully utilize the datapaths in the vector lanes. In this paper, we propose vector lane threading (VLT), an architectural enhancement that allows idle vector lanes to run short-vector or scalar threads. VLTenhanced vector hardware can exploit both data-level and thread-level parallelism to achieve higher performance. We investigate implementation alternatives for VLT, focusing mostly on the instruction issue bandwidth requirements. We demonstrate that VLT’s area overhead is small. For applications with short vectors, VLT leads to additional speedup of 1.4 to 2.3 over the base vector design. For scalar threads, VLT outperforms a 2-way CMP design by a factor of two. Overall, VLT allows vector processors to reach high computational throughput for a wider range of parallel programs and become a competitive alternative to CMP systems.

[Eh. Brief] : [Bad Cannstatt] ; 01.03.1654 / M. Johann Jacobus Strölin Diaconus [manu propria]

Abstract-Models of computers' power consumption enable a variety of energy-efficiency optimizations and reduce data center instrumentation costs. In this paper, we present Composable, Highly Accurate, OS-based (CHAOS) full-system power models for machines and clusters. CHAOS models, which use high-level OS performance counters, yield highly accurate predictions without the intrusiveness and portability problems of hardware counters or board-level instrumentation. Furthermore, they are automatically generated by a lowoverhead software framework (less than 1% CPU utilization on a mobile-class processor). We evaluate CHAOS models using MapReduce-style workloads, executed on server-class systems as well as energyefficient low-power desktops, laptops, and embedded systems. We also generate and validate a generic, cross-platform feature set for cluster power models. To facilitate comparisons across different models and platforms, we define a metric called Dynamic Range Error (DRE) to describe how well the model characterizes the dynamic system behavior. Using this metric, we quantify the tradeoffs between model complexity and accuracy for different workloads. Our results show that the generic cross-platform feature set degrades prediction accuracy by at most 1% DRE compared to power models using the best cluster-specific feature set. To the best of our knowledge, this is the most complete study of system power modeling covering such a wide variety of platforms, workloads, and models