The Cilk++ concurrency platform

Leiserson, Charles E.

doi:10.1145/1629911.1630048

Cited by 153 publications

(108 citation statements)

References 21 publications

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“…Cilk and its later iterations [1,18] are languages whose underlying execution model is more fine grain than classical shared-memory models. However, they do not implement dataflow features to express computations in terms of data and/or event dependencies.…”

Section: Related Workmentioning

confidence: 99%

An Implementation of the Codelet Model

Suettlerlein

Zuckerman

Gao

2013

Euro-Par 2013 Parallel Processing

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Abstract. Chip architectures are shifting from few, faster, functionally heavy cores to abundant, slower, simpler cores to address pressing physical limitations such as energy consumption and heat expenditure. As architectural trends continue to fluctuate, we propose a novel program execution model, the Codelet model, which is designed for new systems tasked with efficiently managing varying resources. The Codelet model is a fine-grained dataflow inspired model extended to address the cumbersome resources available in new architectures. In the following, we define the Codelet execution model as well as provide an implementation named DARTS. Utilizing DARTS and two predominant kernels, matrix multiplication and the Graph 500's breadth first search, we explore the validity of fine-grain execution as a promising and viable execution model for future and current architectures. We show that our runtime is on par or performs better than AMD's highly-optimized parallel library for matrix multication, outperforming it on average by 1.40× with a speedup up to 4×. Our implementation of the parallel BFS outperforms Graph 500's reference implementation (with or without dynamic scheduling) on average by 1.50× with a speed up of up to 2.38×.

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Section: Related Workmentioning

confidence: 99%

An Implementation of the Codelet Model

Suettlerlein

Zuckerman

Gao

2013

Euro-Par 2013 Parallel Processing

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“…OpenMP [33], Intel TBB [4], Cilk[++] [22,61] or even languages like Erlang [94], task scheduling has begun to move outside that realm with the rise of frameworks for cluster and cloud computing.…”

Section: Task Schedulingmentioning

confidence: 99%

Runtime Adaptation for Autonomic Heterogeneous Computing

Scogland

Feng

2014

2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

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Heterogeneity is increasing across all levels of computing, with the rise of accelerators such as GPUs, FPGAs, and other coprocessors into everything from cell phones to supercomputers. More quietly it is increasing with the rise of NUMA systems, hierarchical caching, OS noise, and a myriad of other factors. As heterogeneity becomes a fact of life, efficiently managing heterogeneous compute resources is becoming a critical, and ever more complex, task. The focus of this dissertation is to lay the foundation for an autonomic system for heterogeneous computing, employing runtime adaptation to improve performance portability and performance consistency while maintaining or increasing programmability. We investigate heterogeneity arising from a myriad of factors, grouped into the dimensions of locality and capability. This work has resulted in runtime schedulers capable of automatically detecting and mitigating heterogeneity in physically homogeneous systems through MPI and adaptive coscheduling for physically heterogeneous accelerator based systems as well as a synthesis of the two to address multiple levels of heterogeneity as a coherent whole. We also discuss our current work towards the next generation of fine-grained scheduling and synchronization across heterogeneous platforms in the design of a highly-scalable and portable concurrent queue for many-core systems. Each component addresses aspects of the urgent need for automated management of the extreme and ever expanding complexity introduced by heterogeneity. I have also had the good fortune to collaborate with both Lawrence Livermore National Laboratory and Argonne National Laboratory. Experiences as an intern at each lab has had a significant effect on the final shape of my dissertation. Special thanks go to Dr. Pavan Balaji (again), Dr. Bronis de Supinski (again) and Dr. Barry Rountree. These collaborations proved to be major turning points for me, both in my research and my life as a whole.

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“…The difficulty in writing multithreaded parallel programs has lead to the development of higher-level abstractions for parallel programming, such as task-parallel languages [6,21,13,3], domainspecific parallel languages, pattern libraries that hide lowlevel threads from the programmer [33] and task-parallel programming models [7,31,2,42,42]. Compared to the traditional thread programming, task-parallel programs are easier to write, more portable, and scale better, because the parallelism is not hard-wired into the program, but created at runtime, as needed.…”

Section: Introductionmentioning

confidence: 99%

TProf: An energy profiler for task-parallel programs

Manousakis

Zakkak

Pratikakis

et al. 2015

Sustainable Computing: Informatics and Systems

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We present TProf, an energy profiling tool for OpenMP-like task-parallel programs. To compute the energy consumed by each task in a parallel application, TProf dynamically traces the parallel execution and uses a novel technique to estimate the per-task energy consumption. To achieve this estimation, TProf apportions the total processor energy among cores and overcomes the limitation of current works which would otherwise make parallel accounting impossible to achieve. We demonstrate the value of TProf by characterizing a set of task parallel programs, where we find that data locality, memory access patterns and task working sets are responsible for significant variance in energy consumption between seemingly homogeneous tasks. In addition, we identify opportunities for fine-grain energy optimization by applying per-task Dynamic Voltage and Frequency Scaling (DVFS).

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The Cilk++ concurrency platform

Cited by 153 publications

References 21 publications

An Implementation of the Codelet Model

An Implementation of the Codelet Model

Runtime Adaptation for Autonomic Heterogeneous Computing

TProf: An energy profiler for task-parallel programs

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