Cache-coherent multiprocessors with distributed shared memory are becoming increasingly popular for parallel computing. However, obtaining high performance on these machines mquires that an application execute with good data locality. In addition to making efiective use of caches, it is often necessary to distribute data structures across the local memories of the processing nodes, thereby reducing the latency of cache misses.We have designed a set of abstractions for performing data distribution in the context of explicitly parallel programs and implemented them within the SGI MIPSpro compiler system. Our system incorporates many unique features to enhance both programmability and performance. We address the former by providing a very simple programmming model with extensive support for error detection. Regarding performance, we carefully design the user abstractions with the underlying compiler optimizations in mind, we incorporate several optimization techniques to generate efficient code for accessing distributed data, and we provide a tight integration of these techniques with other optimizations within the compiler Our initial experience suggests that the directives are easy to use and can yield substantial performance gains, in some cases by as much as a factor of 3 over the same codes without distribution.

Data-parallel languages, such as High Performance Fortran, are designed to make programming of distributed-memory machines easier, and resulting programs more portable and efficient. Advanced features of these languages require new methods in both compilers and run-time systems. We present efficient techniques for generating local memory addresses, in the exact order as specified by the original program, for computations involving references to arrays with cyclic(k) distribution, the most general regular data distribution provided in data-parallel languages. Our method exploits the repetitive pattern of memory accesses to handle arbitrary affine subscripts, while minimizing the space and time overhead. Extensive experimental results indicate the efficiency of our approach in practice. 1 Introduction Distributed-memory machines are widely regarded as the most promising means for high performance computing. However, the message-passing programming model, typically associated with these ...

Data-parallel languages, such as High Performance Fortran, are widely regarded as a promising means for writing portable programs for distributed-memory machines. Novel features of these languages call for the development of new techniques in both compilers and run-time systems. In this paper, we present an improved algorithm for finding the local memory access sequence in computations involving regular sections of arrays with cyclic(k) distributions. After establishing the fact that regular section indices correspond to elements of an integer lattice, we show how to find a lattice basis that allows for simple and fast enumeration of memory accesses. The complexity of our algorithm is shown to be lower than that of the previous solution for the same problem. In addition, the experimental results demonstrate the efficiency of our method in practice.

Abstract We describe a compiler and run‐time system that allow data‐parallel programs to execute on a network of heterogeneous UNIX workstations. The programming language supported is Dataparallel C, a SIMD language with virtual processors and a global name space. This parallel programming environment allows the user to take advantage of the power of multiple workstations without adding any message‐passing calls to the source program. Because the performance of Individual workstations in a multi‐user environment may change during the execution of a Dataparallel C program, the run‐time system automatically performs dynamic load balancing. We present experimental results that demonstrate the usefulness of dynamic load‐balancing In a multi‐user environment These results suggest that initially allocating the same amount of work to each processor and letting the dynamic load balancing algorithm adjust the load during program execution yields very good performance. Hence neither the compiler nor the run‐time system need a priori knowledge of the speeds of the machines that will participate in a program execution.

The authors describe a compiler and run-time system that allows data-parallel programs to execute on a network of heterogeneous UNIX workstations. The programming language supported is Dataparallel C, a SIMD language with virtual processors and a global name space. This parallel programming environment allows the user to take advantage of the power of multiple workstations without adding any message-passing calls to the source program. Because the performance of individual workstations in a multi-user environment may change during the execution of a Dataparallel C program, the run-time system automatically performs dynamic load balancing. The authors present experimental results that demonstrate the usefulness of dynamic load balancing in a multi-user environment.>