Data-parallel Programming Research Articles

A type system for implicit scaling

We describe a novel application of subtyping in which a small orthogonal set of structural subtyping rules are used to capture the notion of scaling—an unusual variety of polymorphism found in APL-like languages which is attracting renewed interest due to its applications in data parallel programming. The subtyping approach allows us to provide a simple coercion-based semantics for an orthogonal interpretation of scaling whose generality goes well beyond what is available in APL dialects. Moreover, the results are independent of the structure to which scaling is applied—they apply equally well to lists, vectors, trees, or to any other structure usable for representing sets.

Data-parallel programming on MIMD computers

The implementation of two compilers for the data-parallel programming language Dataparallel C is described. One compiler generates code for Intel and nCUBE hypercube multicomputers; the other generates code for Sequent multiprocessors. A suite of Dataparallel C programs has been compiled and executed, and their execution times and speedups on the Intel iPSC/2, the nCUBE 3200 and the Sequent Symmetry are presented. >

Size and access inference for data-parallel programs

Size and access inference for data-parallel programs

Models for practical parallel computation

A major reason for the lack of practical use of parallel computers has been the absence of a suitable model of parallel computation. Many existing models are either theoretical or are tied to a particular architecture. A more general model must be architecture independent, must realistically reflect execution costs, and must reduce the cognitive overhead of managing massive parallelism. A growing number of models meeting some of these goals have been suggested. We discuss their properties and relative strengths and weaknesses. We conclude that data parallelism is a style with much to commend it, and discuss the Bird-Meertens formalism as a coherent approach to data parallel programming.

Data-parallel programming on multicomputers

The inadequacies of conventional parallel languages for programming multicomputers are identified. The C* language is briefly reviewed, and a compiler that translates C* programs into C programs suitable for compilation and execution on a hypercube multicomputer is presented. Results illustrating the efficiency of executing data-parallel programs on a hypercube multicomputer are reported. They show the speedup achieved by three hand-compiled C* programs executing on an N-Cube 3200 multicomputer. The first two programs, Mandelbrot set calculation and matrix multiplication, have a high degree of parallelism and a simple control structure. The C* compiler can generate relatively straightforward code with performance comparable to hand-written C code. Results for a C* program that performs Gaussian elimination with partial pivoting are also presented and discussed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The heart of object-oriented concurrent programming

The heart of object-oriented concurrent programming

Compiling Fortran 8x array features for the connection machine computer system

The Connection Machine® computer system supports a data parallel programming style, making it a natural target architecture for Fortran 8x array constructs. The Connection Machine Fortran compiler generates VAX code that performs scalar operations and directs the Connection Machine to perform array operations. The Connection Machine virtual processor mechanism supports elemental operations on very large arrays. Most array operators and intrinsic functions map into single instructions or short instruction sequences. Noncontiguous array sections, array-valued subscripts, and parallel constructs such as WHERE and FORALL are also readily accommodated on the Connection Machine. In addition to such customary optimizations as common subexpression elimination, the CM Fortran compiler minimizes data motion for aligning array operations, minimizes transfers between the Connection Machine and the VAX and minimizes context switching for masked computations.