Publications

Abstract not provided.

This paper explores dynamic load balancing algorithms used by asynchronous many-task (AMT), or 'taskbased', programming models to optimize task placement for scientific applications with dynamic workload imbalances. AMT programming models use overdecomposition of the computational domain. Overdecompostion provides a natural mechanism for domain developers to expose concurrency and break their computational domain into pieces that can be remapped to different hardware. This paper explores fully distributed load balancing strategies that have shown great promise for exascalelevel computing but are challenging to theoretically reason about and implement effectively. We present a novel theoretical analysis of a gossip-based load balancing protocol and use it to build an efficient implementation with fast convergence rates and high load balancing quality. We demonstrate our algorithm in a nextgeneration plasma physics application (EMPIRE) that induces time-varying workload imbalance due to spatial non-uniformity in particle density across the domain. Our highly scalable, novel load balancing algorithm, achieves over a 3x speedup (particle work) compared to a bulk-synchronous MPI implementation without load balancing.

We present the execution model of Virtual Transport (VT) a new, Asynchronous Many-Task (AMT) runtime system that provides unprecedented integration and interoperability with MPI. We have developed VT in conjunction with large production applications to provide a highly incremental, high-value path to AMT adoption in the dominant ecosystem of MPI applications, libraries, and developers. Our aim is that the'MPI+X' model of hybrid parallelism can smoothly extend to become'MPI+VT +X'. We illustrate a set of design and implementation techniques that have been useful in building VT. We believe that these ideas and the code embodying them will be useful to others building similar systems, and perhaps provide insight to how MPI might evolve to better support them. We motivate our approach with two applications that are adopting VT and have begun to benefit from increased asynchrony and dynamic load balancing.

Abstract not provided.

Abstract not provided.

Abstract not provided.

The goal of this report is to provide a comprehensive status report of the research & development conducted in the context of the DARMA project by the end of the first quarter of fiscal year 2020. It follows in particular [LBS+19] and [PL19].

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

This is the DARMA FY19-Q1 interim report. This page intentionally left blank. This document was generated with the Automatic Report Generator (ARG). This page intentionally left blank.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

We present an approach to optimize the cache locality for recursive programs by dynamically splicing-recursively interleaving-the execution of distinct function invocations. By utilizing data effect annotations, we identify concurrency and data reuse opportunities across function invocations and interleave them to reduce reuse distance. We present algorithms that efficiently track effects in recursive programs, detect interference and dependencies, and interleave execution of function invocations using user-level (non-kernel) lightweight threads. To enable multi-core execution, a program is parallelized using a nested fork/join programming model. Our cache optimization strategy is designed to work in the context of a random work-stealing scheduler. We present an implementation using the MIT Cilk framework that demonstrates significant improvements in sequential and parallel performance, competitive with a state-of-the-art compile-time optimizer for loop programs and a domainspecific optimizer for stencil programs.

Abstract not provided.

Task-based execution models have received considerable attention in recent years to meet the performance challenges facing high-performance computing (HPC). In this paper we introduce MetaPASS-Metaprogramming-enabled Para-llelism from Apparently Sequential Semantics-a proof-of-concept, non-intrusive header library that enables implicit task-based parallelism in a sequential C++ code. MetaPASS is a data-driven model, relying on dependency analysis of variable read-/write accesses to derive a directed acyclic graph (DAG) of the computation to be performed. MetaPASS enables embedding of runtime dependency analysis directly in C++ applications using only template metaprogramming. Rather than requiring verbose task-based code or source-to-source compilers, a native C++ code can be made task-based with minimal modifications. We present an overview of the programming model enabled by MetaPASS and the C++ runtime API required to support it. Details are provided regarding how standard template metaprogramming is used to capture task dependencies. We finally discuss how the programming model can be deployed in both an MPI+X and in a standalone distributed memory context.

Abstract not provided.

Abstract not provided.