XMT is a multi-threaded programming model designed to exploit explicit specification of parallel threads. Its main features are a simple thread execution model and an efficient prefix-sum instruction for synchronizing shared data accesses. This paper presents and evaluates the performance of multithreading in the XMT programming environment. A prototype XMT compiler converts parallel regions into procedure calls, which are then executed efficiently in XMT hardware. An architecture simulator similar to SimpleScalar is used to evaluate the performance of the XMT system for twelve benchmark codes. Results show the XMT architecture generally succeeds in providing low-overhead parallel threads and uniform access times on-chip. However, compiler optimizations to cluster (coarsen) threads are still needed for very fine-grained threads. Keywords Parallel programming, compilers, processor architectures. 1. Introduction Conditional branches, variable memory access latencies, and other barrie...

Explicit-multithreading (XMT) is a parallel programming model designed for exploiting on-chip parallelism. Its features include a simple thread execution model and an efficient prefix-sum instruction for synchronizing shared data accesses. By taking advantage of low-overhead parallel threads and high on-chip memory bandwidth, the XMT model tries to reduce the burden on programmers by obviating the need for explicit task assignment and thread coarsening. This paper presents features of the XMT programming model, and evaluates their utility through experiments on a prototype XMT compiler and architecture simulator. We find the lack of explicit task assignment has slight effects on performance for the XMT architecture. Despite low thread overhead, thread coarsening is still necessary to some extent, but can usually be automatically applied by the XMT compiler. The prefix-sum instruction provides more scalable synchronization than traditional locks, and the simple run-until-completion thread execution model (no busywaits) does not impair performance. Finally, the combination of features in XMT can encourage simpler parallel algorithms that may be more efficient than more traditional complex approaches. 1.