ALPS: Software Framework for Scheduling Parallel Computationswith Application to Parallel Space-Time Adaptive Processing

Kyusoon Lee and Adam W. BojanczykCornell UniversityIthaca, NY, 14850

{KL224,AWB8}@cornell.edu

High Performance Embedded Computing (HPEC) Workshop18 – 20 September 2007

/* This is a .cpp file of the partially adaptive STAP methods that the user had in mind.

This is optimized for minimum latency or maximum throughput on a given parallel machine.*/

#include <alps.h>...

Various STAP heuristicsconstructed from ALPS building blocks

Modeling execution times ofbasic building blocks under various configurations

Find optimal configuration that minimizesoverall makespan or maximizes throughput

ALPS CodeGenUser’s idea on

Parallel Computations(e.g. STAP)

Task Graph

ALPS Benchmarker & Modeler

ALPS Scheduler

ALPS Library

ALPS Software Framework

Building execution time models using incremental weighted least-squares

Measurements are not quite reliable

Number of variables in the model is largeDifficulties

0 0.1 0.2 0.3 0.4 0.5 0.6 0.70

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Relative errors from ordinary least-square

Relative errors from weighted least-square

10% be

tter

20% be

tter

30% be

tter

40% be

tter

7.5% errorfrom INV-weightedleast-square

26.6% errorfrom ordinaryleast-square

Scheduling tree-shaped task graphs on parallel computers exploiting mixed-parallelism

Communication cost not negligible

Arbitrary computation costs

Trade-off between task-parallelism and data-parallelism

Difficulties

0

10

20

30

Data-parallelismonly

Task-parallelismpreferred

Ourscheduling algorithm

Makespan (sec)

estimated achieved

0

0.05

0.1

Data-parallelismonly

Task-parallelismpreferred

Ourscheduling algorithm

Throughput (1/sec)

estimated

achieved