Xizhou Feng

PowerPack: Energy Profiling and Analysis of High-Performance Systems and Applications

Energy efficiency is a major concern in modern high-performance computing system design. In the past few years, there has been mounting evidence that power usage limits system scale and computing density, and thus, ultimately system performance. However, despite the impact of power and energy on the computer systems community, few studies provide insight to where and how power is consumed on high-performance systems and applications. In previous work, we designed a framework called PowerPack that was the first tool to isolate the power consumption of devices including disks, memory, NICs, and processors in a high-performance cluster and correlate these measurements to application functions. In this work, we extend our framework to support systems with multicore, multiprocessor-based nodes, and then provide in-depth analyses of the energy consumption of parallel applications on clusters of these systems. These analyses include the impacts of chip multiprocessing on power and energy efficiency, and its interaction with application executions. In addition, we use PowerPack to study the power dynamics and energy efficiencies of dynamic voltage and frequency scaling (DVFS) techniques on clusters. Our experiments reveal conclusively how intelligent DVFS scheduling can enhance system energy efficiency while maintaining performance.

EpiSimdemics: an efficient algorithm for simulating the spread of infectious disease over large realistic social networks

Preventing and controlling outbreaks of infectious diseases such as pandemic influenza is a top public health priority. We describe EpiSimdemics - a scalable parallel algorithm to simulate the spread of contagion in large, realistic social contact networks using individual-based models. EpiSimdemics is an interaction-based simulation of a certain class of stochastic reaction-diffusion processes. Straightforward simulations of such process do not scale well, limiting the use of individual-based models to very small populations. EpiSimdemics is specifically designed to scale to social networks with 100 million individuals. The scaling is obtained by exploiting the semantics of disease evolution and disease propagation in large networks. We evaluate an MPI-based parallel implementation of EpiSimdemics on a mid-sized HPC system, demonstrating that EpiSimdemics scales well. EpiSimdemics has been used in numerous sponsor defined case studies targeted at policy planning and course of action analysis, demonstrating the usefulness of EpiSimdemics in practical situations.

CPU MISER: A Performance-Directed, Run-Time System for Power-Aware Clusters

Performance and power are critical design constraints in todays high-end computing systems. Reducing power consumption without impacting system performance is a challenge for the HPC community. We present a runtime system (CPU MISER) and an integrated performance model for performance-directed, power-aware cluster computing. CPU MISER supports system-wide, application-independent, fine-grain, dynamic voltage and frequency scaling (DVFS) based power management for a generic power-aware cluster. Experimental results show that CPU MISER can achieve as much as 20% energy savings for the NAS parallel benchmarks. In addition to energy savings, CPU MISER is able to constrain performance loss for most applications within user-specified limits. These constraints are achieved through accurate performance modeling and prediction, coupled with advanced control techniques.

Power and energy profiling of scientific applications on distributed systems

Power consumption is a troublesome design constraint for emergent systems such as IBMs BlueGene /L. If current trends continue, future petaflop systems will require 100 megawatts of power to maintain high-performance. To address this problem the power and energy characteristics of high-performance systems must be characterized. To date, power-performance profiles for distributed systems have been limited to interactive commercial workloads. However, scientific workloads are typically non-interactive (batched) processes riddled with interprocess dependences and communication. We present a framework for direct, automatic profiling of power consumption for non-interactive, parallel scientific applications on high-performance distributed systems. Though our approach is general, we use our framework to study the power-performance efficiency of the NAS parallel benchmarks on a 32-node Beowulf cluster. We provide profiles by component (CPU, memory, disk, and NIC), by node (for each of 32 nodes), and by system scale (2, 4, 8, 16, and 32 nodes). Our results indicate power profiles are often regular corresponding to application characteristics and for fixed problem size increasing the number of nodes always increases energy consumption but does not always improve performance. This finding suggests smart schedulers could be used to optimize for energy while maintaining performance.

EpiFast: a fast algorithm for large scale realistic epidemic simulations on distributed memory systems

Large scale realistic epidemic simulations have recently become an increasingly important application of high-performance computing. We propose a parallel algorithm, EpiFast, based on a novel interpretation of the stochastic disease propagation in a contact network. We implement it using a master-slave computation model which allows scalability on distributed memory systems. EpiFast runs extremely fast for realistic simulations that involve: (i) large populations consisting of millions of individuals and their heterogeneous details, (ii) dynamic interactions between the disease propagation, the individual behaviors, and the exogenous interventions, as well as (iii) large number of replicated runs necessary for statistically sound estimates about the stochastic epidemic evolution. We find that EpiFast runs several magnitude faster than another comparable simulation tool while delivering similar results. EpiFast has been tested on commodity clusters as well as SGI shared memory machines. For a fixed experiment, if given more computing resources, it scales automatically and runs faster. Finally, EpiFast has been used as the major simulation engine in real studies with rather sophisticated settings to evaluate various dynamic interventions and to provide decision support for public health policy makers.

High-performance, power-aware distributed computing for scientific applications

The PowerPack framework enables distributed systems to profile, analyze, and conserve energy in scientific applications using dynamic voltage scaling. For one common benchmark, the framework achieves more than 30 percent energy savings with minimal performance impact.

Improvement of power-performance efficiency for high-end computing

Left unchecked, the fundamental drive to increase peak performance using tens of thousands of power hungry components will lead to intolerable operating costs and failure rates. Recent work has shown application characteristics of single-processor, memory-bound non-interactive codes and distributed, interactive Web services can be exploited to conserve power and energy with minimal performance impact. Our novel approach is to exploit parallel performance inefficiencies characteristic of non-interactive, distributed scientific applications, conserving energy using DVS (dynamic voltage scaling) without impacting time-to-solution (ITS) significantly, reducing cost and improving reliability. We present a software framework to analyze and optimize distributed power-performance using DVS implemented on a 16-node Centrino-based cluster. Using various DVS strategies we achieve application-dependent overall system energy savings as large as 25% with as little as 2% performance impact.

Parallel algorithms for Bayesian phylogenetic inference

The combination of a Markov chain Monte Carlo (MCMC) method with likelihood-based assessment of phylogenies is becoming a popular alternative to direct likelihood optimization. However, MCMC, like maximum likelihood, is a computationally expensive method. To approximate the posterior distribution of phylogenies, a Markov chain is constructed, using the Metropolis algorithm, such that the chain has the posterior distribution of the parameters of phylogenies as its stationary distribution.This paper describes parallel algorithms and their MPI-based parallel implementation for MCMC-based Bayesian phylogenetic inference. Bayesian phylogenetic inference is computationally expensive both in time and in memory requirements. Our variations on MCMC and their implementation were done to permit the study of large phylogenetic problems. In our approach, we can distribute either entire chains or parts of a chain to different processors, since in current models the columns of the data are independent. Evaluations on a 32-node Beowulf cluster suggest the problem scales well. A number of important points are identified, including a superlinear speedup due to more effective cache usage and the point at which additional processors slow down the process due to communication overhead.

Modeling and evaluating energy-performance efficiency of parallel processing on multicore based power aware systems

In energy efficient high end computing, a typical problem is to find an energy-performance efficient resource allocation for computing a given workload. An analytical solution to this problem includes two steps: first estimating the performances and energy costs for the workload running with various resource allocations, and second searching the allocation space to identify the optimal allocation according to an energy-performance efficiency measure. In this paper, we develop analytical models to approximate performance and energy cost for scientific workloads on multicore based power aware systems. The performance models extend Amdahls law and power-aware speedup model to the context of multicore-based power aware computing. The power and energy models describe the power effects of resource allocation and workload characteristics. As a proof of concept, we show model parameter derivation and model validation using performance, power, and energy profiles collected on a prototype multicore based power aware cluster.

Modeling interaction between individuals, social networks and public policy to support public health epidemiology

Human behavior, social networks, and civil infrastructure are closely intertwined. Understanding their co-evolution is critical for designing public policies. Human behaviors and day-to-day activities of individuals create dense social interactions that provide a perfect fabric for fast disease propagation. Conversely, peoples behavior in response to public policies and their perception of the crisis can dramatically alter normally stable social interactions. Effective planning and response strategies must take these complicated interactions into account. The basic problem can be modeled as a coupled co-evolving graph dynamical system and can also be viewed as partially observable Markov decision process. As a way to overcome the computational hurdles, we describe an High Performance Computing oriented computer simulation to study this class of problems. Our method provides a novel way to study the co-evolution of human behavior and disease dynamics in very large, realistic social networks with over 100 Million nodes and 6 Billion edges.