Distributed shared memory (DSM) systems could overcome major obstacles of the widespread use of distributed-memory multiprocessors, while retaining the attractive features of low cost and good scalability common to distributed-memory machines. A DSM system allows a natural and portable programming model on distributed-memory machines, making it possible to construct a relatively inexpensive and scalable parallel system on which programmers can develop parallel application codes. Due to its potential advantages, DSM has received increasing attention. In this panel, challenges in building efficient DSM systems for a wide range of applications are addressed and discussed.

No abstract available.

Parallel processing has been a subject of extensive research for over 20 years, especially in the last 10 years, with many commercial parallel machines becoming available, from small scale parallel machines to massively parallel machines. At one time, it was claimed that parallel machines will become the mainstream computers. However, more recently, some parallel computer vendors have gone out of business and some others are struggling. Some pessimists even claimed that this is a dying field. So, what’s wrong? Five distinguished panelists are invited to share their views on this issue. The panelists are also expected to address what could be done and could be done in order to make parallel computers truly mainstream computers. Panelists

High-performance computing is moving towards commercially available teraflop platforms. The various attributes of this movement will be examined. Chip technology, interconnect, programming model, as well as data management will be explored. Future trends as they relate to these features will also be analyzed.

A description is given of the C240, a tightly coupled, shared memory, parallel multiprocessor that supports up to 40-ns ECL/CMOS Cray-like processors. It is managed by a fully semaphored Unix operating system and can support up to 4 Gb of directly addressable physical memory. Convex proprietary compiler technology provides automatic vectorization and parallelization for Fortran, C and Ada. The allocation of parallel threads to physical processors is managed by ASAP (automatic self-allocating processors), which dynamically allocates and deallocates parallel threads to the processors.>