Title page for ETD etd-04192011-170837

Type of Document Dissertation
Author Dekate, Chirag
Author's Email Address cdekate@gmail.com
URN etd-04192011-170837
Title Extreme Scale Parallel NBody Algorithm With Event Driven Constraint Based Execution Model
Degree Doctor of Philosophy (Ph.D.)
Department Computer Science
Advisory Committee
Advisor Name Title
Sterling, Thomas Committee Chair
Chen, Jianhua Committee Member
Iyengar, Sitharama Committee Member
Kaiser, Hartmut Committee Member
Ramanujam, J. Committee Member
Ram, Yitzak Dean's Representative
  • barnes hut
  • nbody simulations
  • high performance computing
  • Exascale
  • HPX
  • ParalleX
Date of Defense 2011-04-11
Availability unrestricted
Traditional scientific applications such as Computational Fluid Dynamics,

Partial Differential Equations based numerical methods (like Finite Difference

Methods, Finite Element Methods) achieve sufficient efficiency on state of the

art high performance computing systems and have been widely studied / implemented using

conventional programming models. For emerging application domains such as Graph applications

scalability and efficiency is significantly constrained by the conventional systems and

their supporting programming models.

Furthermore technology trends like multicore, manycore,

heterogeneous system architectures are introducing new challenges and possibilities.

Emerging technologies are requiring a rethinking of approaches to more effectively

expose the underlying parallelism to the applications and the end-users.

This thesis explores the space of effective parallel execution of ephemeral graphs

that are dynamically generated. The standard particle based simulation,

solved using the Barnes-Hut algorithm is chosen to exemplify the

dynamic workloads.

In this thesis the workloads are expressed using sequential execution semantics,

a conventional parallel programming model - shared memory semantics and semantics

of an innovative execution model designed for efficient scalable performance towards

Exascale computing called ParalleX. The main outcomes of this research are parallel

processing of dynamic ephemeral workloads, enabling dynamic load balancing during runtime, and

using advanced semantics for exposing parallelism in scaling constrained applications.

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