Efficient, portable implementation of asynchronous multi-place programs

Bikshandi, Ganesh; Castaños, José G.; Kodali, Sreedhar; Nandivada, V. Krishna; Peshansky, Igor; Saraswat, Vijay; Sur, Sayantan; Varma, Pradeep; Wen, Tong

doi:10.1145/1594835.1504215

Cited by 10 publications

(12 citation statements)

References 13 publications

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“…The construct "finish {...}" defines a scope at the end of which all assemblies created within the scope must join. We do not allow "finish"-blocks to be nested within "async isolated" blocks, thus following the "flat" model of "finish" statements studied in [2]. However, isolated asynchronous blocks can be nested to an arbitrary depth.…”

Section: Syntaxmentioning

confidence: 99%

Delegated isolation

et al. 2011

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Isolation-the property that a task can access shared data without interference from other tasks-is one of the most basic concerns in parallel programming. In this paper, we present Aida, a new model of isolated execution for parallel programs that perform frequent, irregular accesses to pointer-based shared data structures. The three primary benefits of Aida are dynamism, safety and liveness guarantees, and programmability. First, Aida allows tasks to dynamically select and modify, in an isolated manner, arbitrary fine-grained regions in shared data structures, all the while maintaining a high level of concurrency. Consequently, the model can achieve scalable parallelization of regular as well as irregular shared-memory applications. Second, the model offers freedom from data races, deadlocks, and livelocks. Third, no extra burden is imposed on programmers, who access the model via a simple, declarative isolation construct that is similar to that for transactional memory.The key new insight in Aida is a notion of delegation among concurrent isolated tasks (known in Aida as assemblies). Each assembly A is equipped with a region in the shared heap that it owns-the only objects accessed by A are those it owns, guaranteeing race-freedom. The region owned by A can grow or shrink flexibly-however, when A needs to own a datum owned by B, A delegates itself, as well as its owned region, to B. From now on, B has the responsibility of re-executing the task A set out to complete. Delegation as above is the only inter-assembly communication primitive in Aida. In addition to reducing contention in a local, Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. data-driven manner, it guarantees freedom from deadlocks and livelocks.We offer an implementation of Aida on top of the Habanero Java parallel programming language. The implementation employs several novel ideas, including the use of a union-find data structure to represent tasks and the regions that they own. A thorough evaluation using several irregular data-parallel benchmarks demonstrates the low overhead and excellent scalability of Aida, as well as its benefits over existing approaches to declarative isolation. Our results show that Aida performs on par with the state-of-the-art customized implementations of irregular applications and much better than coarse-grained locking and transactional memory approaches.

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Section: Syntaxmentioning

confidence: 99%

Delegated isolation

et al. 2011

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“…A region is a set of points, and can be used to specify an array allocation or an iteration range as in the case of forall. Each dynamic instance of a forall statement includes a distinct phaser object (say, ph) that is created implicitly, and is set up so that all iterations in the forall are registered on ph in signal-wait mode 4 . Since the scope of ph is limited to the implicit finish in the forall, the parent task will drop its registration on ph after all the forall iterations are created.…”

Section: Forallmentioning

confidence: 99%

“…4 For readers familiar with the foreach statement in X10 and HJ, one way to relate forall to foreach is to think of forall stmt as syntactic sugar for "ph=new phaser(); finish foreach phased (ph) stmt ". tial programs [16]:…”

Section: Classical Loop Transformationsmentioning

confidence: 99%

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Reducing task creation and termination overhead in explicitly parallel programs

Zhao

Shirako

Nandivada

et al. 2010

Proceedings of the 19th International Conference on Parallel Architectures and Compilation Techniques

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There has been a proliferation of task-parallel programming systems to address the requirements of multicore programmers. Current production task-parallel systems include Cilk++, Intel Threading Building Blocks, Java Concurrency, .Net Task Parallel Library, OpenMP 3.0, and current research task-parallel languages include Cilk, Chapel, Fortress, X10, and Habanero-Java (HJ). It is desirable for the programmer to express all the parallelism intrinsic to their algorithm in their code for forward scalability and portability, but the overhead incurred by doing so can be prohibitively large in today's systems. In this paper, we address the problem of reducing the total amount of overhead incurred by a program due to excessive task creation and termination. We introduce a transformation framework to optimize task-parallel programs with finish, forall and next statements. Our approach includes elimination of redundant task creation and termination operations as well as strength reduction of termination operations (finish) to lighter-weight synchronizations (next). Experimental results were obtained on three platforms: a dual-socket 128-thread (16-core) Niagara T2 system, a quad-socket 16-way Intel Xeon SMP and a quad-socket 32-way Power7 SMP. The results showed maximum speedup of 66.7×, 11.25× and 23.1× respectively on each platform and 4.6×, 2.1× and 6.4× performance improvements respectively in geometric mean related to non-optimized parallel codes. The original benchmarks in this study were written with medium-grained parallelism; a larger relative improvement can be expected for programs written with finer-grained parallelism. However, even for the medium-grained parallel benchmarks studied in this paper, the significant improvement obtained by the transformation framework underscores the importance of the compiler optimizations introduced in this paper.

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“…Flat-X10 adds multiplace spawn processes [3]. Habanero's Phaser adds support for asynchronous collectives in X10 [23].…”

Section: Related Workmentioning

confidence: 99%

Hiding latency in Coarray Fortran 2.0

Scherer

Adhianto

Jin

et al. 2010

Proceedings of the Fourth Conference on Partitioned Global Address Space Programming Model

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In Numrich and Reid's 1998 proposal [17], Coarray Fortran is a simple set of extensions to Fortran 95, principal among which is support for shared data known as coarrays. Responding to shortcomings in the Fortran Standards Committee's addition of coarrays to the Fortran 2008 standards, we at Rice envisioned an extensive update which has come to be known as Coarray Fortran 2.0 [15]. In this paper, we chronicle the evolution of Coarray Fortran 2.0 as it gains support for asynchronous point-to-point and collective operations. We outline how these operations are implemented and describe code fragments from several benchmark programs to show we use these operations to hide latency by overlapping communication and computation.

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Efficient, portable implementation of asynchronous multi-place programs

Cited by 10 publications

References 13 publications

Delegated isolation

Delegated isolation

Reducing task creation and termination overhead in explicitly parallel programs

Hiding latency in Coarray Fortran 2.0

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