A Load Balancing Framework for Adaptive and Asynchronous

ApplicationsKevin Barker, Andrey Chernikov, Nikos

Chrisochoides,Keshav Pingali ;IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL. 1

5, NO. 2, pp. 183-192 Feb. 2004Presented by 張肇烜

Outline

• Introduction

• Software Framework and Programming Model

• Load Balancing Library

• Performance Evaluation

• Related Work

• Conclusions

Introduction

• Stop-and-repartition method:– Load-balancing is accomplished by

dynamically repartitioning the data after the global synchronization phases.

– Synchronization overhead can overwhelm the benefits of improved load balance.

Introduction (cont.)

• PREMA:– Parallel Runtime Environment for Multicompu

ter Applications.– Single-sided communication similar to that pro

vided by Active Messages.– A framework which allows for the easy and eff

icient implementation of customized dynamic load balancing algorithms.

Introduction (cont.)

• Active Messages:– An efficient communication architecture for

multiprocessors.– Efficient supports a variety of parallel program

models, including message passing , shared memory and dataflow.

Software Framework and Programming Model

• Data Movement and Control Substrate (DMCS) :– DMCS provides a flexible and easy to

understand application program interface for one-sided communication operations.

Software Framework and Programming Model (cont.)

• Mobile Object Layer (MOL):– A runtime substrate for parallel adaptive and

irregular computations.– A mobile object may be referenced by any

processor in the parallel system by using its associated mobile pointer.

Load Balancing Library

• PREMA’s ILB component library is built using the framework provided by DMCS and the MOL.

• Supports the rapid development of algorithms, allowing researchers to experiment without modification of existing application code.

Load Balancing Library (cont.)

• Load balancing methods:– Diffusion– Master-worker– Work-stealing– Multilist

Performance Evaluation

Performance Evaluation (cont.)

Performance Evaluation (cont.)

Related Work

Related Work (cont.)

• Zoltan:– Zonltan provides graph-based partitioning algo

rithms and several geometric load balancing algorithms.

• CHARM++:– CHARM++ is built on an underlying language

which is a dialect of C++ and provides extensive dynamic load balancing strategies.

Conclusions

• We have demonstrated performance improvements of 15% over traditional stop and repartition methods , 30% over intrusive explicit load balancing methods , and 42% over no load balancing on configurations of 128 processors.