Programming Abstractions and Toolchain for Dataflow Multithreading Architectures

Stavrou, Kyriakos; Pavlou, Demos; Nikolaides, Marios; Petrides, Panayiotis; Evripidou, Paraskevas; Trancoso, Pedro; Popovic, Zdravko; Giorgi, Roberto

doi:10.1109/ispdc.2009.35

Cited by 13 publications

(2 citation statements)

References 10 publications

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“…More recently, Evripidou et al proposed a new programming model called Data-Driven Multithreading (DDM) [15], based on dataflow model of execution. Subsequent works include tools for exploiting DDM on commodity multicores [19], [20], and the usage of DDM in HPC [4]. Targeting function level parallelism, Gupta and Sohi applied dataflow analysis to generate multithreaded code from imperative programs [8].…”

Section: Related Workmentioning

confidence: 99%

An Approach for Direct Dataflow Execution on Contemporary Multicore Systems

Loghin

Tudor

Teo

2013

2013 Data-Flow Execution Models for Extreme Scale Computing

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Abstract-Traditionally, imperative programming uses a series of state-based operands to model control-flow and, as a result, suffers from the well-known von Neumann bottleneck. In contrast, dataflow programs are driven only by the availability of instruction operands. However, the lack of mainstream dataflow hardware hinders direct dataflow instruction execution. On the other hand, direct execution of dataflow programs on von Neumann machines incurs a high performance cost. In this paper, we present preliminary results on the direct execution of dataflow programs on multicore systems through emulation of taggedtokens mechanism. Compared with direct translation of SISAL programs to C code, we achieved a speedup of 44 for CPUintensive applications and 22 for memory-bounded applications on a 48-cores AMD NUMA system.

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Section: Related Workmentioning

confidence: 99%

An Approach for Direct Dataflow Execution on Contemporary Multicore Systems

Loghin

Tudor

Teo

2013

2013 Data-Flow Execution Models for Extreme Scale Computing

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“…Dataflow runtime systems have emerged as appealing solutions to create parallel programs for multi‐ and many‐core environments 19,27‐31 . Dataflow paradigm is virtually supported on traditional multicore machines, where each core is a virtual processing element (PE) that runs the dataflow firing rule.…”

Section: Introductionmentioning

confidence: 99%

Gamma — General Abstract Model for Multiset mAnipulation and dynamic dataflow model: An equivalence study

Mello

Araújo

Alves

et al. 2021

Concurrency and Computation

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With the increase of the search for computational models where the expression of parallelism occurs naturally, some paradigms arise as options for the current generation of computers. In this context, dynamic dataflow and Gamma—General Abstract Model for Multiset mAnipulation—emerge as interesting computational model choices. In dynamic dataflow model, operations are performed as soon as their associated operands are available, without rely on a Program Counter to dictate the execution order of instructions. The Gamma paradigm is based on a parallel multiset rewriting scheme. It provides a nondeterministic execution model inspired by an abstract chemical machinemetaphor, where operations are formulated as reactions that occur freely among matching elements belonging to the multiset. In this work, equivalence relations between the dynamic dataflow and Gamma paradigms are exposed and explored, while methods to convert from dataflow to Gamma paradigm and vice versa are provided. It is shown that vertices and edges of a dynamic dataflow graph can correspond, respectively, to reactions and multiset elements in the Gamma paradigm. This work provides the scientific community with the possibility of taking profit of both parallel programming models, contributing with a versatility component to researchers and developers.

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