Scalable Multiprocessor Architecture Research Articles

Design of a scalable multiprocessor architecture and its simulation

Performance enhancement and system scalability are two of the most important issues in the design of multiprocessor systems. A scalable cluster-based multiprocessor architecture and its simulation environment called SEECMA are proposed. Several new issues in our architecture, including scalable cache coherence protocols, relaxed memory consistency models, memory optimization techniques and several types of processors are considered. It has been developed to meet current trends in clustering architecture design. Additionally, the SEECMA environment is presented as a helpful investigation tool for both education and research. In addition to many simulation options, it is provided with a user-friendly graphic interface. SEECMA can automatically collect data from several simulation runs and display the results for comparison. So far, we have evaluated several vital issues of cluster-based multiprocessors on SEECMA including effective prefetching and replacement policies, and optimization of migratory sharing using both hardware and software mechanisms. On average, these enhance system performance by up to 8%, 9% and 7%, respectively. Our cluster-based multiprocessor architecture also scales more readily than the current general, or cluster-based, multiprocessor environments.

On-line distributed debugging on scalable multiprocessor architectures

Debugging parallel programs is one of the most tedious jobs in programming scalable multiprocessor architectures. Due to the distributed resources of these machines, programming is often architecture dependent. Most development tools still reflect this dependency even during the analysis phase of parallel programs. This paper presents the distributed debugger DETOP, which offers a global name space and hides architectural features like the mapping of processes. DETOP is part of the integrated tool environment TOPSYS implemented on iPSC hypercubes, networks of SPARCstations and partly on Transputer and PowerPC based systems.

Performance benefits and limitations of large NUMA multiprocessors

In scalable multiprocessor architectures, the times required for a processor to access various portions of memory are different. In this paper, we consider how this characteristic affects performance by comparing it to the ideal but unrealizable case in which the access times to all memory modules can be kept constant, even as the number of processors is increased. We examine several application kernels to investigate how well they would execute on various instances of NUMA systems with a hierarchical memory structure. The results of our analytic model show that access locality is much more important in NUMA architectures than it is in UMA architectures. The extent of the performance penalty of non-local memory accesses depends on the variability in access times to various parts of shared memory, as well as on the amount of congestion in the interconnection network that provides access to remote memory modules. In the applications we examined, we found that it is possible to partition and locate both the data and the computation in such a way that reasonable speedups can be achieved on NUMA systems.

An abstract machine to implement or-and parallel PROLOG efficiently

PEPSys (Parallel ECRC PROLOG System) is a research project started in 1984 in the Computer Architecture Group of the European Computer-Industry Research Centre (ECRC). Its general goals are to study and evaluate new and practicable solutions to the problems of parallel logic programming. The PEPSys Abstract Machine described in this paper was designed to allow an efficient implementation of the PEPSys computational model. Based on the WAM, it incorporates a number of novel features to support the management of the logical variable and the control of the search space of the PEPSys computational model. Both a parallel implementation on a multiprocessor and a simulation system of scalable multiprocessor architectures implement the PEPSys Abstract Machine and yield effective speedups in parallel computations.