Towards an Optimized Heterogeneous Distributed Task Scheduler in OpenMP Cluster

This paper addresses the challenges of optimizing task scheduling for a distributed, task-based execution model in OpenMP for cluster computing environments. Traditional OpenMP implementations are primarily designed for shared-memory parallelism and offer limited control over task scheduling. Howeve...

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Published in:SC24-W: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis pp. 1894 - 1903
Main Authors: Neveu, Remy, Ceccato, Rodrigo, Leite, Gustavo, Araujo, Guido, Diaz, Jose M. Monsalve, Yviquel, Herve
Format: Conference Proceeding
Language:English
Published: IEEE 17.11.2024
Subjects:
Cluster computing
Distributed architectures
Dynamic scheduling
Iterative methods
Large-scale systems
Optimal scheduling
Parallel processing
Parallel programming
Parallel systems
Resource management
Runtime
Scheduling algorithms
Scheduling and task partitioning
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Abstract This paper addresses the challenges of optimizing task scheduling for a distributed, task-based execution model in OpenMP for cluster computing environments. Traditional OpenMP implementations are primarily designed for shared-memory parallelism and offer limited control over task scheduling. However, improved scheduling mechanisms are critical to achieving performance and portability in distributed and heterogeneous environments. OpenMP Cluster (OMPC) was introduced to overcome these limitations, extending OpenMP with the Heterogeneous Earliest Finish Time (HEFT) task scheduling algorithm tailored for large-scale systems. To improve scheduling and enable better system utilization, the runtime system must resolve challenges such as changes in the application balance, amount of parallelism, and varying communication latencies.This work presents three key contributions: first, the refactoring of the OMPC runtime to unify task scheduling across devices and hosts; second, the optimization of the HEFT-based scheduling algorithm to ensure efficient task execution in distributed environments; and third, an extensive evaluation of Work Stealing and HEFT scheduling mechanisms in real-world clusters. While the HEFT implementation in OMPC is not fully optimized, this work provides a significant step toward improving distributed task scheduling in cluster computing, offering insights and incremental advancements that support the development of scalable and high-performance applications. Results show improvements of up to 24% in scheduling time while opening up to more extensions in the scheduling methods.
AbstractList This paper addresses the challenges of optimizing task scheduling for a distributed, task-based execution model in OpenMP for cluster computing environments. Traditional OpenMP implementations are primarily designed for shared-memory parallelism and offer limited control over task scheduling. However, improved scheduling mechanisms are critical to achieving performance and portability in distributed and heterogeneous environments. OpenMP Cluster (OMPC) was introduced to overcome these limitations, extending OpenMP with the Heterogeneous Earliest Finish Time (HEFT) task scheduling algorithm tailored for large-scale systems. To improve scheduling and enable better system utilization, the runtime system must resolve challenges such as changes in the application balance, amount of parallelism, and varying communication latencies.This work presents three key contributions: first, the refactoring of the OMPC runtime to unify task scheduling across devices and hosts; second, the optimization of the HEFT-based scheduling algorithm to ensure efficient task execution in distributed environments; and third, an extensive evaluation of Work Stealing and HEFT scheduling mechanisms in real-world clusters. While the HEFT implementation in OMPC is not fully optimized, this work provides a significant step toward improving distributed task scheduling in cluster computing, offering insights and incremental advancements that support the development of scalable and high-performance applications. Results show improvements of up to 24% in scheduling time while opening up to more extensions in the scheduling methods.
Author Neveu, Remy
Leite, Gustavo
Diaz, Jose M. Monsalve
Yviquel, Herve
Araujo, Guido
Ceccato, Rodrigo
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  givenname: Rodrigo
  surname: Ceccato
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  givenname: Herve
  surname: Yviquel
  fullname: Yviquel, Herve
  email: hyviquel@unicamp.br
  organization: Universidade Estadual de Campinas (UNICAMP),Instituto de Computação,Campinas,Brazil
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Snippet This paper addresses the challenges of optimizing task scheduling for a distributed, task-based execution model in OpenMP for cluster computing environments....
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StartPage 1894
SubjectTerms Cluster computing
Distributed architectures
Dynamic scheduling
Iterative methods
Large-scale systems
Optimal scheduling
Parallel processing
Parallel programming
Parallel systems
Resource management
Runtime
Scheduling algorithms
Scheduling and task partitioning
Title Towards an Optimized Heterogeneous Distributed Task Scheduler in OpenMP Cluster
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