Exploiting domain knowledge to optimize parallel computational mechanics codes

  • Authors:

  • Chenyang Liu;Muhammad Hasan Jamal;Milind Kulkarni;Arun Prakash;Vijay Pai

  • Affiliations:

  • Purdue University, West Lafayette, IN, USA;Purdue University, West Lafayette, IN, USA;Purdue University, West Lafayette, IN, USA;Purdue University, West Lafayette, IN, USA;Purdue University, West Lafayette, IN, USA

  • Venue:
  • Proceedings of the 27th international ACM conference on International conference on supercomputing
  • Year:
  • 2013

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Abstract

An important emerging problem domain in computational science and engineering is the development of multi-scale computational methods for complex problems in mechanics that span multiple spatial and temporal scales. An attractive approach to solving these problems is recursive decomposition: the problem is broken up into a tree of loosely coupled sub-problems which can be solved independently and then coupled back together to obtain the desired solution. However, a particular problem can be solved in myriad ways by coupling the sub-problems together in different tree orders. As we argue in this paper, the space of possible orders is vast, the performance gap between an arbitrary order and the best order is potentially quite large, and the likelihood that a domain scientist can find the best order to solve a problem on a particular machine is vanishingly small. In this paper, we present a system that uses domain-specific knowledge captured in computational libraries to optimize code written in a conventional language (C). The system generates efficient coupling orders to solve computational mechanics problems using recursive decomposition. Our system adopts the inspector-executor paradigm, where the problem is inspected and a novel heuristic finds an effective implementation based on domain properties evaluated by a cost model. The derived implementation is then executed by a parallel run-time system (Cilk) which achieves optimal parallel performance. We demonstrate that our cost model is highly correlated with actual application runtime, that our proposed technique outperforms non-decomposed and non-multiscale methods. The code generated by the heuristic also outperforms alternate scheduling strategies, as well as over 99% of randomly-generated recursive decompositions sampled from the space of possible solutions.