While the common trend in building large-scale multiprocessors is to use commodity compute nodes that are increasingly powerful and have deep memory hierarchies, the Tera MTA uses a different design point, with a relatively flat memory system, no processor caches, and hardware support for lightweight multithreading, which is used to mask memory latency.In this paper we explore the implementation of Titanium, a language with coarse-grained SPMD parallelism, onto the MTA.The major concerns in obtaining high performance on the MTA are sufficient degrees of parallelism, good load balance, and low synchronization overhead.We show that by adding loop level parallelism, Titanium applications have sufficient parallelism for the MTA, and as expected, application writers do not need to orchestrate data layout.We evaluate multiple implementations of the Titanium synchronization constructs, which include barriers and monitors.We then explore several scheduling strategies, and find that the distinction between SPMD and loop level parallelism proves to be surprisingly useful.The two-level parallelism structure can be used to throttle thread migration, which lowers thread creation overhead and synchronization.We use a combination of micro-benchmarks and applications to demonstrate these results.

Titanium is a language and system for high-performance parallel scientific computing. Titanium uses Java as its base, thereby leveraging the advantages of that language and allowing us to focus attention on parallel computing issues. The main additions to Java are immutable classes, multidimensional arrays, an explicitly parallel SPMD model of computation with a global address space, and zone-based memory management. We discuss these features and our design approach, and report progress on the development of Titanium, including our current driving application: a three-dimensional adaptive mesh refinement parallel Poisson solver. © 1998 John Wiley & Sons, Ltd.