Raj Panda

Performance projection of HPC applications using SPEC CFP2006 benchmarks

Performance projections of High Performance Computing (HPC) applications onto various hardware platforms are important for hardware vendors and HPC users. The projections aid hardware vendors in the design of future systems, enable them to compare the application performance across different existing and future systems, and help HPC users with system procurement and application refinements. In this paper, we present a method for projecting the node level performance of HPC applications using published data of industry standard benchmarks, the SPEC CFP2006, and hardware performance counter data from one base machine. In particular, we project performance of eight HPC applications onto four systems, utilizing processors from different vendors, using data from one base machine, the IBM p575. The projected performance of the eight applications was within 7.2% average difference with respect to measured runtimes for IBM POWER6 systems and standard deviation of 5.3%. For two Intel based systems with different micro-architecture and Instruction Set Architecture (ISA) than the base machine, the average projection difference to measured runtimes was 10.5% with standard deviation of 8.2%.

Turbulence in a randomly stirred fluid

The results of direct numerical simulations of a fluid stirred by the Gaussian random force 〈 fifj 〉 ∝k−3 are reported. It is shown that the mean properties of the random force generated turbulence are reasonably close to those observed in physical experiments. It is shown that higher‐order moments of temporal and spatial velocity derivatives are dominated by different factors. It is shown that there is an intermediate range of wavenumbers with spatial and temporal spectra that fit E(k)∝k−5/3 and E(ω)∝ω−2. This suggests the breakdown of the random Taylor hypothesis in the inertial range of homogenous, isotropic turbulence at moderate Reynolds numbers.

Optimizing performance and energy of HPC applications on POWER7

Power consumption is a critical consideration in high performance computing systems and it is becoming the limiting factor to build and operate Petascale and Exascale systems. When studying the power consumption of existing systems running HPC workloads, we find power, energy and performance are closely related leading to the possibility to optimize energy without sacrificing (much or at all) performance.This paper presents the power features of the POWER7 and shows how innovative software can use these features to optimize the power and energy consumptions of large cluster running HPC workloads.This paper starts by presenting the new features which have been introduced in POWER7 to manage power consumption and the tools available to manage and record the power consumption. We then analyze the power consumption and performance of different HPC workloads at various levels of the POWER7 server (processor, memory, io) for different frequencies. We propose a model to predict both the power and energy consumption of real workloads based on their performance characteristics measured by hardware performance counters (HPM). We show that the power estimation model can achieve less than 5% error versus actual measurements. In conclusion, we present how an innovative scheduler can help to optimize both power and energy consumptions of large HPC clusters.

SWAPP: A Framework for Performance Projections of HPC Applications Using Benchmarks

Surrogate-based Workload Application Performance Projection (SWAPP) is a framework for performance projections of High Performance Computing (HPC) applications using benchmark data. Performance projections of HPC applications onto various hardware platforms are important for hardware vendors and HPC users. The projections aid hardware vendors in the design of future systems and help HPC users with system procurement. SWAPP assumes that one has access to a base system and only benchmark data for a target system, the target system is not available for running the HPC application. Projections are developed using the performance profiles of the benchmarks and application on the base system and the benchmark data for the target system. SWAPP projects the performances of compute and communication components separately then combine the two projections to get the full application projection. In this paper SWAPP was used to project the performance of three NAS Multi-Zone benchmarks onto three systems (an IBM POWER6 575 cluster and an IBM Intel West mere x5670 both using an Infiniband interconnect and an IBM Blue Gene/P with a 3D Torus and Collective Tree interconnects), the base system is an IBM POWER5+ 575 cluster. The projected performance of the three benchmarks was within 11.44% average error magnitude and standard deviation of 2.64% for the three systems.