Experiments with List Ranking for Explicit Multi-Threaded (XMT) Instruction Parallelism

Abstract

Algorithms for the problem of list ranking are empirically studied with respect to the Explicit Multi-Threaded (XMT) platform for instruction-level parallelism (ILP). The main goal of this study is to understand the differences between XMT and more traditional parallel computing implementation platforms/models as they pertain to the well studied list ranking problem. The main two findings are: (i) Good speedups for much smaller inputs are possible. (ii) In part, this finding is based on competitive performance by a new variant of a 1984 algorithm, called the No-Cut algorithm. The paper incorporates analytic (non-asymptotic) performance analysis into experimental performance analysis for relatively small inputs. This provides an interesting example where experimental research and theoretical analysis complement one another.¹

Explicit Multi-Threading (XMT) is a fine-grained computation framework introduced in our SPAA’98 paper. Building on some key ideas of parallel computing, XMT covers the spectrum from algorithms through architecture to implementation; the main implementation related innovation in XMT was through the incorporation of low-overhead hardware and software mechanisms (for more effective fine-grained parallelism). The reader is referred to that paper for detail on these mechanisms. The XMT platform aims at faster single-task completion time by way of ILP.

Partially supported by NSF grant CCR-9416890 at U. Maryland.