Daniel A. Reed

Autopilot: adaptive control of distributed applications

With increasing development of applications for heterogeneous, distributed computing grids, the focus of performance analysis has shifted from a posteriori optimization on homogeneous parallel systems to application tuning for heterogeneous resources with time varying availability. This shift has profound implications for performance instrumentation and analysis techniques. Autopilot is a new infrastructure for dynamic performance tuning of heterogeneous computational grids based on closed loop control. The paper describes the Autopilot model of distributed sensors, actuators, and decision procedures, reports preliminary performance benchmarks, and presents a case study in which the Autopilot library is utilized in the development of an adaptive parallel input/output system.

Scalable performance analysis: the Pablo performance analysis environment

Developers of application codes for massively parallel computer systems face daunting performance tuning and optimization problems that must be solved if massively parallel systems are to fulfill their promise. Recording and analyzing the dynamics of application program, system software, and hardware interactions is the key to understanding and the prerequisite to performance tuning, but this instrumentation and analysis must not unduly perturb program execution. Pablo is a performance analysis environment designed to provide unobtrusive performance data capture, analysis, and presentation across a wide variety of scalable parallel systems. Current efforts include dynamic statistical clustering to reduce the volume of data that must be captured and complete performance data immersion via head-mounted displays.<<ETX>>

New grid scheduling and rescheduling methods in the GrADS project

The goal of the Grid Application Development Software (GrADS) Project is to provide programming tools and an execution environment to ease program development for the Grid. This paper presents recent extensions to the GrADS software framework: a new approach to scheduling workflow computations, applied to a 3-D image reconstruction application; a simple stop/migrate/restart approach to rescheduling Grid applications, applied to a QR factorization benchmark; and a process-swapping approach to rescheduling, applied to an N-body simulation. Experiments validating these methods were carried out on both the GrADS MacroGrid (a small but functional Grid) and the MicroGrid (a controlled emulation of the Grid).

PPFS: a high performance portable parallel file system

James V. Huber, Jr.* Christopher L. Elford* Daniel A. Reed* Andrew A. Chien* David S. Blumenthal* Department of Computer Science University of Illinois Urbana, Illinois 61801 Rapid increases in processor performance over the past decade have outstripped performance improvements in input /output devices, increasing the import ante of input /output performance to overall system performance. Further, experience has shown that the performance of parallel input /output systems is particularly sensitive to data placement and data management policies, making good choices critical. To explore this vast design space, we have developed a user-level library, the Portable Parallel File System ( PP.FS), which supports rapid experimentation and exploration. The PPFS includes a rich application interface, allowing the application to advertise access patterns, control caching and prefet thing, and even control data placement. PPFS is both extensible and portable, making possible a wide range of experiments on a broad variety of platforms and configurations. Our initial experiments, based on simple benchmarks and two application programs, show that tailoring policies to input/output access patterns yields significant performance benefits, often improving performance by nearly an order of magnitude.

The Autopilot performance-directed adaptive control system

Abstract High-performance computing is rapidly expanding to include distributed collections of heterogeneous sequential and parallel systems and irregular applications with complex, data dependent execution behavior and time-varying resource demands. To provide adaptive resource management for dynamic applications, we are developing the Autopilot toolkit. Autopilot provides a flexible set of performance sensors, decision procedures, and policy actuators to realize adaptive control of applications and resource management policies on both parallel and wide area distributed systems.

Exascale computing and big data

Scientific discovery and engineering innovation requires unifying traditionally separated high-performance computing and big data analytics.

Toward a framework for preparing and executing adaptive grid programs

This paper describes the program execution framework being developed by the Grid Application Development Software (GrADS) Project. The goal of this framework is to provide good resource allocation for Grid applications and to support adaptive reallocation if performance degrades because of changes in the availability of Grid resources. At the heart of this strategy is the notion of a configurable object program, which contains, in addition to application code, strategies for mapping the application to different collections of resources and a resource selection model that provides an estimate of the performance of the application on a specific collection of Grid resources. This model must be accurate enough to distinguish collections of resources that will deliver good performance from those that will not. The GrADS execution framework also provides a contract monitoring mechanism for interrupting and remapping an application execution when performance falls below acceptable levels.

SvPablo: A multi-language architecture-independent performance analysis system

In this paper we present the design of SvPablo, a language independent performance analysis and visualization system that can be easily extended to new contexts with minimal changes to the software infrastructure. At present, SvPablo supports analysis of applications written in C, Fortran 77, Fortran 90, and HPF on a variety of sequential and parallel systems. In addition to capturing application data via software instrumentation, SvPablo also exploits hardware performance counters to capture the interaction of software and hardware. Both hardware and software performance data are summarized during program execution, enabling measurement of programs that execute for hours or days on hundreds of processors. This performance data is stored in a format designed to be language transparent and portable. We demonstrate the usefulness of SvPablo for tuning application programs with a case study running on an SGI Origin 2000.

Automatic ARIMA time series modeling for adaptive I/O prefetching

Inadequate I/O performance remains a major challenge in using high-end computing systems effectively. To address this problem, we present TsModeler, an automatic time series modeling and prediction framework for adaptive I/O prefetching that uses ARIMA time series models to predict the temporal patterns of I/O requests. These online pattern analysis techniques and cutoff indicators for autocorrelation patterns enable multistep online predictions suitable for multiblock prefetching. This work also combines time series predictions with spatial Markov model predictions to determine when, what, and how many blocks to prefetch. Experimental results show reductions in execution time compared to the standard Linux file system across various hardware configurations.

Performance Contracts: Predicting and Monitoring Grid Application Behavior

Given the dynamic nature of grid resources, adaptation is required to sustain a predictable level of application performance. A prerequisite of adaptation is the recognition of changing conditions. In this paper we introduce an application signature model and performance contracts to specify expected grid application behavior, and discuss our monitoring infrastructure that detects when actual behavior does not meet expectations. Experimental results are given for several scenarios.