Managing Heterogeneity in a Grid Parallel Haskell

Abstract

Computational Grids potentially offer cheap large-scale high-performance systems, but are a very challenging architecture, being heterogeneous, shared and hierarchical. Rather than requiring a programmer to explicitly manage this complex environment, we recommend using a high-level parallel functional language, like gph, with largely automatic management of parallel coordination.

We present GridGUM, an initial port of the distributed virtual shared-memory implementation of gph for computational grids. We show that, GridGUM delivers acceptable speedups on relatively low latency homogeneous and heterogeneous computational Grids. Moreover, we find that for heterogeneous computational grids, load management limits performance.

We present the initial design of GridGUM2, that incorporates new
load management mechanisms that cheaply and effectively combine
static and dynamic information to adapt to heterogeneous grids.
The mechanisms are evaluated by measuring four non-trivial programs
with different parallel properties. The measurements show that the
new mechanisms improve load distribution over the original
implementation, reducing runtime by factors ranging from 17 % to
57 %, and the greatest improvement is obtained for the most
dynamic program.