Susan Coghlan

Abstract We investigate operating system noise, which we identify as one of the main reasons for a lack of synchronicity in par- allel applications. Using a microbenchmark, we measure the noise on several contemporary platforms and find that, even with a general-purpose operating system, noise can be limited if certain precautions are taken. We then inject ar- tificially generated noise

ABSTRACT Power consumption is becoming a critical factor as we continue our quest toward exascale computing. Yet, actual power utilization of a complete system is an insufficiently studied research area. Estimating the power consumption of a large scale system is a nontrivial task because a large number of components are involved and because power requirements are affected by the (unpredictable) workloads. Clearly needed is a power-monitoring infrastructure that can provide timely and accurate feedback to system developers and application writers so that they can optimize the use of this precious resource. Many existing large-scale installations do feature power-monitoring sensors, however, those are part of environmental- and health monitoring sub systems and were not designed with application level power consumption measurements in mind. In this paper, we evaluate the existing power monitoring of IBM Blue Gene systems, with the goal of understanding what capabilities are available and how they fare with respect to spatial and temporal resolution, accuracy, latency, and other characteristics. We find that with a careful choice of dedicated micro benchmarks, we can obtain meaningful power consumption data even on Blue Gene/P, where the interval between available data points is measured in minutes. We next evaluate the monitoring subsystem on Blue Gene/Q, and are able to study the power characteristics of FPU and memory subsystems of Blue Gene/Q. We find the monitoring subsystem capable of providing second-scale resolution of power data conveniently separated between node components with seven seconds latency. This represents a significant improvement in power monitoring infrastructure, and hope future systems will enable real-time power measurement in order to better understand application behavior at a finer granularity.

ABSTRACT A varied collection of scientific and engineering codes has been adapted and enhanced to take advantage of the IBM Blue Gene®/Q architecture and thus enable research that was previously out of reach. Computational research teams from a number of disciplines collaborated with the staff of the Argonne Leadership Computing Facility to assess which of Blue Gene/Q's many novel features could be exploited for each application to equip it to tackle existing problem classes with greater fidelity and in some cases to address new phenomena. The quad floating-point units and the five-dimensional torus interconnect are among the features that were demonstrated to be effective for a number of important applications. Furthermore, data obtained from the hardware counters provided insights that were valuable in guiding the code modifications. Hardware features and programming techniques that were effective across multiple codes are documented as well. First, we have confirmed that there is no significant code rewrite needed to run today's production codes with good performance on Mira, an IBM Blue Gene/Q supercomputer. Performance improvements are already demonstrated, even though our measurements are all on pre-production software and hardware. The application domains included biology, materials science, combustion, chemistry, nuclear physics, and industrial-scale design of nuclear reactors, jet engines, and the efficiency of transportation systems.