Optimal mapping of sequences of data parallel tasks

Subhlok, Jaspal; Vondran, Gary

doi:10.1145/209936.209951

Cited by 46 publications

(66 citation statements)

References 9 publications

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“…If the computations of a given stage are independent from one data set to another, two consecutive computations (different data sets) for the same stage can be mapped onto distinct processors, thus reducing the period for the processing of this stage. Such a stage can be replicated, using the terminology of Subhlok and Vondran [27,28] and of the DataCutter team [6,7,26]. This corresponds to the dealable stages of Cole [11].…”

Section: Working Out An Examplementioning

confidence: 99%

“…If the computations of the k-th stage are independent from one data set to another, the k-th stage S k can be replicated [26][27][28]: several consecutive computations are mapped onto distinct processors. Data sets are processed in a round robin fashion by these processors.…”

Section: Replicated Stage/taskmentioning

confidence: 99%

“…Such workflows operate on a collection of data sets that are executed in [26,27,31]. Each data set is input to the application graph and traverses it until its processing is complete.…”

Section: Introductionmentioning

confidence: 99%

“…For example Subhlok and Vondran [27,28] have been able to design dynamic pro-gramming algorithms for bi-criteria mappings on homogeneous platforms. Also, if we neglect all communication costs, minimizing the period amounts to solve the well-known chains-to-chains problem.…”

Section: Introductionmentioning

confidence: 99%

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Complexity Results for Throughput and Latency Optimization of Replicated and Data-parallel Workflows

Benoît

Robert

2008

Algorithmica

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Mapping applications onto parallel platforms is a challenging problem, even for simple application patterns such as pipeline or fork graphs. Several antagonist criteria should be optimized for workflow applications, such as throughput and latency (or a combination). In this paper, we consider a simplified model with no communication cost, and we provide an exhaustive list of complexity results for different problem instances. Pipeline or fork stages can be replicated in order to increase the throughput by sending consecutive data sets onto different processors. In some cases, stages can also be data-parallelized, i.e. the computation of one single data set is shared between several processors. This leads to a decrease of the latency and an increase of the throughput. Some instances of this simple model are shown to be NP-hard, thereby exposing the inherent complexity of the mapping problem. We provide polynomial algorithms for other problem instances. Altogether, we provide solid theoretical foundations for the study of mono-criterion or bi-criteria mapping optimization problems.

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Section: Working Out An Examplementioning

confidence: 99%

Section: Replicated Stage/taskmentioning

confidence: 99%

“…Such workflows operate on a collection of data sets that are executed in [26,27,31]. Each data set is input to the application graph and traverses it until its processing is complete.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Complexity Results for Throughput and Latency Optimization of Replicated and Data-parallel Workflows

Benoît

Robert

2008

Algorithmica

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“…There are many prior works that use similar models to estimate throughput for linear chains of tasks [13] [14].…”

Section: Illustrating Examplesmentioning

confidence: 99%

A Throughput-Driven Task Creation and Mapping for Network Processors

Liu¹,

Li²,

Chen

2007

High Performance Embedded Architectures and Compilers

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Abstract. Network processors are programmable devices that can process packets at a high speed. A network processor is typified by multithreading and heterogeneous multiprocessing, which usually requires programmers to manually create multiple tasks and map these tasks onto different processing elements. This paper addresses the problem of automating task creation and mapping of network applications onto the underlying hardware to maximize their throughput. We propose a throughput cost model to guide the task creation and mapping with the objective of both minimizing the number of stages in the processing pipeline and maximizing the average throughput of the slowest task simultaneously. The average throughput is modeled by taking communication cost, computation cost, memory access latency and synchronization cost into account. We envision that programmers write small functions for network applications, such that we use grouping and duplication to construct tasks from the functions. The optimal solution of creating tasks from m functions and mapping them to n processors is an NP-hard problem. Therefore, we present a practical and efficient heuristic algorithm with an O((n + m)m) complexity and show that the obtained solutions produce excellent performance for typical network applications. The entire framework has been implemented in the Open Research Compiler (ORC) adapted to compile network applications written in a domain-specific dataflow language. Experimental results show that the code produced by our compiler can achieve the 100% throughput on the OC-48 input line rate. OC-48 is a fiber optic connection that can handle a 2.488Gbps connection speeds, which is what our targeted hardware was designed for. We also demonstrate the importance of good creation and mapping choices on achieving high throughput. Furthermore, we show that reducing communication cost and efficient resource management are the most important factors for maximizing throughput on the Intel IXP network processors.

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Special Issue on Compilation Techniques for Distributed Memory Systems. Guest Editors' Introduction

Pande

Agrawal

1996

Journal of Parallel and Distributed Computing

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Optimal mapping of sequences of data parallel tasks

Cited by 46 publications

References 9 publications

Complexity Results for Throughput and Latency Optimization of Replicated and Data-parallel Workflows

Complexity Results for Throughput and Latency Optimization of Replicated and Data-parallel Workflows

A Throughput-Driven Task Creation and Mapping for Network Processors

Special Issue on Compilation Techniques for Distributed Memory Systems. Guest Editors' Introduction

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