15 research outputs found
Finding All Minimum Size Separating Vertex Sets in a Graph
Coordinated Science Laboratory was formerly known as Control Systems LaboratorySemiconductor Research Corporation / 87-DP-10
Easy PRAM-based High-performance Parallel Programming with ICE
A poster of this paper will be presented at the 25th International Conference on Parallel Architecture and Compilation Technology (PACT β16), September 11-15, 2016, Haifa, Israel.Parallel machines have become more widely used. Unfortunately parallel programming
technologies have advanced at a much slower pace except for regular programs. For irregular
programs, this advancement is inhibited by high synchronization costs, non-loop parallelism,
non-array data structures, recursively expressed parallelism and parallelism that is too
fine-grained to be exploitable.
We present ICE, a new parallel programming language that is easy-to-program, since:
(i) ICE is a synchronous, lock-step language; (ii) for a PRAM algorithm its ICE program
amounts to directly transcribing it; and (iii) the PRAM algorithmic theory offers unique
wealth of parallel algorithms and techniques. We propose ICE to be a part of an ecosystem
consisting of the XMT architecture, the PRAM algorithmic model, and ICE itself, that together
deliver on the twin goal of easy programming and efficient parallelization of irregular
programs. The XMT architecture, developed at UMD, can exploit fine-grained parallelism in
irregular programs. We built the ICE compiler which translates the ICE language into the
multithreaded XMTC language; the significance of this is that multi-threading is a feature
shared by practically all current scalable parallel programming languages. As one indication
of ease of programming, we observed a reduction in code size in 7 out of 11 benchmarks
vs. XMTC. For these programs, the average reduction in number of lines of code was when
compared to hand optimized XMTC The remaining 4 benchmarks had the same code size.
Our main result is perhaps surprising: The run-time was comparable to XMTC with a 0.76%
average gain for ICE across all benchmarks.NSF award 116185
Parallel Algorithmic Techniques for Combinatorial Computation
Parallel computation offers the promise of great improvements in the solution of problems that, if we were restricted to sequential computation, would take so much time that solution would be impractical. There is a drawback to the use of parallel computers, however, and that is that they seem to be harder to program. For this reason, parallel algorithms in practice are often restricted to simple problems such as matrix multiplication. Certainly this is useful, and in fact we shall see later some non-obvious uses of matrix manipulation, but many of the large problems requiring solution are of a more complex nature. In particular, an instance of a problem may be structured as an arbitrary graph or tree, rather than in the regular order of a matrix. In this paper we describe a number of algorithmic techniques that have been developed for solving such combinatorial problems. The intent of the paper is to show how the algorithmic tools we present can be used as building blocks for higher level algorithms, and to present pointers to the literature for the reader to look up the specifics of these algorithms. We make no claim to completeness; a number of techniques have been omitted for brevity or because their chief application is not combinatorial in nature. In particular we give very little attention to parallel sorting, although sorting is used as a subroutine in a number of the algorithms we describe. We also only describe algorithms, and not lower bounds, for solving problems in parallel