Paper
:
A Constraint-Based Approach to Automatic Data Partitioning for Distributed Memory Execution
Event Type
Paper
Registration Categories
TP
Tags
Memory
Performance
Resource Management
Scheduling
Task-based programming
Award Finalists
BP Finalist
BSP Finalist
TimeWednesday, 20 November 20191:30pm - 2pm
Location405-406-407
DescriptionAlthough data partitioning is required to enable parallelism on distributed memory systems, data partitions are not first class objects in most distributed programming models. As a result, automatic parallelizers and application writers encode a particular partitioning strategy in the parallelized program, leading to a program not easily configured or composed with other parallel programs.

We present a constraint-based approach to automatic data partitioning. By introducing abstractions for first-class data partitions, we express a space of correct partitioning strategies. Candidate partitions are characterized by partitioning constraints, which can be automatically inferred from data accesses in parallelizable loops. Constraints can be satisfied by synthesized partitioning code or user-provided partitions. We demonstrate that programs auto-parallelized in our approach are easily composed with manually parallelized parts and have scalability comparable to hand-optimized counterparts.
Back To Top Button