Implicitly threaded parallelism in Manticore

Abstract

The increasing availability of commodity multicore processors is making parallel computing available to the masses. Traditional parallel languages are largely intended for large-scale scientific computing and tend not to be well-suited to programming the applications one typically finds on a desktop system. Thus we need new parallel-language designs that address a broader spectrum of applications. In this paper, we present Manticore, a language for building parallel applications on commodity multicore hardware including a diverse collection of parallel constructs for different granularities of work. We focus on the implicitly-threaded parallel constructs in our high-level functional language. We concentrate on those elements that distinguish our design from related ones, namely, a novel parallel binding form, a nondeterministic parallel case form, and exceptions in the presence of data parallelism. These features differentiate the present work from related work on functional data parallel language designs, which has focused largely on parallel problems with regular structure and the compiler transformations -most notably, flattening -that make such designs feasible. We describe our implementation strategies and present some detailed examples utilizing various mechanisms of our language.

Reduced I/O Latency with Futures (Brief Announcement)

“…Interesting Use of Futures: Since its proposal in the late 70th [Baker andHewi , 1977, Friedman andWise, 1978], the use of futures has been incorporated into various task parallel platforms [Cavé et al, 2011, Chandra et al, 1994, Charles et al, 2005, Fluet et al, 2010, Halstead, 1985, Kranz et al, 1989, Spoonhower et al, 2008, Taşırlar and Sarkar, 2011. Futures are typically used as a high-level synchronization construct to allow parallel tasks to coordinate with one another in a way that is more exible than pure fork-join parallelism.…”

Compiling with Continuations and LLVM

“…We support the parallelism and concurrency features of PML by transformations on the AST and BOM representations that introduce explicit continuation binders [15,16,4,25]. The translation from BOM to CPS uses the Danvy-Filinski CPS transformation [7] and we perform several kinds of optimizations on the CPS IR [5,3].…”

“…In particular, we show how to use LLVM to support the heapallocated first-class continuation runtime model [1] used by the SML/NJ system and by the Manticore system [16]. We have integrated our approach into the Parallel ML (PML) compiler that is part of the Manticore project [16]. Initial observations suggest that the LLVM backend produces more efficient code relative to the previous MLRisc [17] backend.…”

Competitive parallelism: getting your priorities right

“…Cooperatively threaded languages such as NESL (Blelloch et al, 1994), Cilk (Frigo et al, 1998), parallel Haskell (Chakravarty et al, 2007Keller et al, 2010) and parallel ML (Fluet et al, 2011;Jagannathan et al, 2010;Raghunathan et al, 2016), have at least two important features:…”