Benchmark-problem instances for static scheduling of task graphs with communication delays on homogeneous multiprocessor systems

Davidović, Tatjana; Crainic, Teodor Gabriel

doi:10.1016/j.cor.2005.01.005

Cited by 52 publications

(43 citation statements)

References 16 publications

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“…Task execution time variations were profiled for different frames. The second benchmark set was a collection of random task graph instances proposed by Davidović et al [6]. These problems were designed to be unbiased towards any particular solver approach and are reportedly harder than other existing benchmarks for scheduling task dependence graphs.…”

Section: Resultsmentioning

confidence: 99%

Scheduling task dependence graphs with variable task execution times onto heterogeneous multiprocessors

Satish

Ravindran

Keutzer

2008

Proceedings of the 8th ACM International Conference on Embedded Software

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We present a statistical optimization approach for a scheduling a task dependence graph with variable task execution times onto a heterogeneous multiprocessor system. Scheduling methods in the presence of variations typically rely on worst-case timing estimates for hard real-time applications, or average-case analysis for other applications. However, a large class of soft real-time applications require only statistical guarantees on latency and throughput. We present a general statistical model that captures the probability distributions of task execution times as well as the correlations of execution times of different tasks. We use a Monte Carlo based technique to perform makespan analysis of different schedules based on this model. This approach can be used to analyze the variability present in a variety of soft real-time applications, including a H.264 video processing application.We present two scheduling algorithms based on statistical makespan analysis. The first is a heuristic based on a critical path analysis of the task dependence graph. The other is a simulated annealing algorithm using incremental timing analysis. Both algorithms take as input the required statistical guarantee, and can thus be easily re-used for different required guarantees. We show that optimization methods based on statistical analysis show a 25-30% improvement in makespan over methods based on static worst-case analysis.

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Section: Resultsmentioning

confidence: 99%

Scheduling task dependence graphs with variable task execution times onto heterogeneous multiprocessors

Satish

Ravindran

Keutzer

2008

Proceedings of the 8th ACM International Conference on Embedded Software

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“…We have tested the two algorithms on the benchmark task graphs of Tatjana and Gabriel [10], [11]. 120 task graphs having number of nodes as 50, 100, 200, and 300 respectively are selected for testing.…”

Section: Resultsmentioning

confidence: 99%

A Clustering Heuristic for Multiprocessor Environments Using Computation and Communication Loads of Modules

Mishra¹,

Mishra²,

Mishra³

2010

IJCSIT

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In this paper, we have developed a heuristic for the task allocation problem on a fully connected homogeneous multiprocessor environment. Our heuristic is based on a value associated with the modules called the Computation-Communication-Load (CCLoad). This value is dependent on the computation and the communication times associated with the module. Using the concept of CCLoad, we propose a clustering algorithm of complexity O(|V| 2 (|V|+|E|)log(|V|+|E|)), and demonstrate its superiority over a generic version of Sarkar's algorithm.

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“…Two sets of synthetic benchmarks were used in the evaluations: 1) Benchmark-I: randomly generated task graphs with communication delays [13], and 2) Benchmark-II: synthetic graphs generated using the DAG generation tool in [14]. More details on the benchmarks can be found in the technical report [12].…”

Section: Synthetic Task Graphsmentioning

confidence: 99%

Toward Optimizing Latency Under Throughput Constraints for Application Workflows on Clusters

Vydyanathan¹,

Çatalyürek

Kurç

et al. 2007

Euro-Par 2007 Parallel Processing

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Abstract. In many application domains, it is desirable to meet some user-defined performance requirement while minimizing resource usage and optimizing additional performance parameters. For example, application workflows with realtime constraints may have strict throughput requirements and desire a low latency or response-time. The structure of these workflows can be represented as directed acyclic graphs of coarse-grained application tasks with data dependences. In this paper, we develop a novel mapping and scheduling algorithm that minimizes the latency of workflows that act on a stream of input data, while satisfying throughput requirements. The algorithm employs pipelined parallelism and intelligent clustering and replication of tasks to meet throughput requirements. Latency is minimized by exploiting task parallelism and reducing communication overheads. Evaluation using synthetic benchmarks and application task graphs shows that our algorithm 1) consistently meets throughput requirements even when other existing schemes fail, 2) produces lower-latency schedules, and 3) results in lesser resource usage.

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Benchmark-problem instances for static scheduling of task graphs with communication delays on homogeneous multiprocessor systems

Cited by 52 publications

References 16 publications

Scheduling task dependence graphs with variable task execution times onto heterogeneous multiprocessors

Scheduling task dependence graphs with variable task execution times onto heterogeneous multiprocessors

A Clustering Heuristic for Multiprocessor Environments Using Computation and Communication Loads of Modules

Toward Optimizing Latency Under Throughput Constraints for Application Workflows on Clusters

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