The Paragraph: Design and Implementation of the STAPL Parallel Task Graph

Parallel programming is becoming mainstream due to the increased availability of multiprocessor and multicore architectures and the need to solve larger and more complex problems. Languages and tools available for the development of parallel applications are often difficult to learn and use. The Standard Template Adaptive Parallel Library (STAPL) is being developed to help programmers address these difficulties. STAPL is a parallel C++ library with functionality similar to STL, the ISO adopted C++ Standard Template Library. STAPL provides a collection of parallel pContainers for data storage and pViews that provide uniform data access operations by abstracting away the details of the pContainer data distribution. Generic pAlgorithms are written in terms of PARAGRAPHs, high level task graphs expressed as a composition of common parallel patterns. These task graphs define a set of operations on pViews as well as any ordering (i.e., dependences) on these operations that must be enforced by STAPL for a valid execution. The subject of this dissertation is the PARAGRAPH Executor, a framework that manages the runtime instantiation and execution of STAPL PARAGRAPHS. We address several challenges present when using a task graph program representation and discuss a novel approach to dependence specification which allows task graph creation and execution to proceed concurrently. This overlapping increases scalability and reduces the resources required by the PARAGRAPH Executor. We also describe the interface for task specification as well as optimizations that address issues such as data locality. We evaluate the performance of the PARAGRAPH Executor on several parallel machines including massively parallel Cray XT4 and Cray XE6 systems and an IBM Power5 cluster. Using tests including generic parallel algorithms, kernels from the NAS NPB suite, and a nuclear particle transport application written in STAPL, we demonstrate that the PARAGRAPH Executor enables STAPL to exhibit good scalability on more than $10^4$ processors

Advances in the Design and Implementation of a Multi-Tier Architecture in the GIPSY Environment

We present advances in the software engineering design and implementation of the multi-tier run-time system for the General Intensional Programming System (GIPSY) by further unifying the distributed technologies used to implement the Demand Migration Framework (DMF) in order to streamline distributed execution of hybrid intensional-imperative programs using Java.Comment: 11 pages, 3 figure

Group Communication Patterns for High Performance Computing in Scala

We developed a Functional object-oriented Parallel framework (FooPar) for high-level high-performance computing in Scala. Central to this framework are Distributed Memory Parallel Data structures (DPDs), i.e., collections of data distributed in a shared nothing system together with parallel operations on these data. In this paper, we first present FooPar's architecture and the idea of DPDs and group communications. Then, we show how DPDs can be implemented elegantly and efficiently in Scala based on the Traversable/Builder pattern, unifying Functional and Object-Oriented Programming. We prove the correctness and safety of one communication algorithm and show how specification testing (via ScalaCheck) can be used to bridge the gap between proof and implementation. Furthermore, we show that the group communication operations of FooPar outperform those of the MPJ Express open source MPI-bindings for Java, both asymptotically and empirically. FooPar has already been shown to be capable of achieving close-to-optimal performance for dense matrix-matrix multiplication via JNI. In this article, we present results on a parallel implementation of the Floyd-Warshall algorithm in FooPar, achieving more than 94 % efficiency compared to the serial version on a cluster using 100 cores for matrices of dimension 38000 x 38000

PaPaS: A Portable, Lightweight, and Generic Framework for Parallel Parameter Studies

The current landscape of scientific research is widely based on modeling and simulation, typically with complexity in the simulation's flow of execution and parameterization properties. Execution flows are not necessarily straightforward since they may need multiple processing tasks and iterations. Furthermore, parameter and performance studies are common approaches used to characterize a simulation, often requiring traversal of a large parameter space. High-performance computers offer practical resources at the expense of users handling the setup, submission, and management of jobs. This work presents the design of PaPaS, a portable, lightweight, and generic workflow framework for conducting parallel parameter and performance studies. Workflows are defined using parameter files based on keyword-value pairs syntax, thus removing from the user the overhead of creating complex scripts to manage the workflow. A parameter set consists of any combination of environment variables, files, partial file contents, and command line arguments. PaPaS is being developed in Python 3 with support for distributed parallelization using SSH, batch systems, and C++ MPI. The PaPaS framework will run as user processes, and can be used in single/multi-node and multi-tenant computing systems. An example simulation using the BehaviorSpace tool from NetLogo and a matrix multiply using OpenMP are presented as parameter and performance studies, respectively. The results demonstrate that the PaPaS framework offers a simple method for defining and managing parameter studies, while increasing resource utilization.Comment: 8 pages, 6 figures, PEARC '18: Practice and Experience in Advanced Research Computing, July 22--26, 2018, Pittsburgh, PA, US

The STAPL Parallel Container Framework

The Standard Template Adaptive Parallel Library (STAPL) is a parallel programming infrastructure that extends C with support for parallelism. STAPL provides a run-time system, a collection of distributed data structures (pContainers) and parallel algorithms (pAlgorithms), and a generic methodology for extending them to provide customized functionality. Parallel containers are data structures addressing issues related to data partitioning, distribution, communication, synchronization, load balancing, and thread safety. This dissertation presents the STAPL Parallel Container Framework (PCF), which is designed to facilitate the development of generic parallel containers. We introduce a set of concepts and a methodology for assembling a pContainer from existing sequential or parallel containers without requiring the programmer to deal with concurrency or data distribution issues. The STAPL PCF provides a large number of basic data parallel structures (e.g., pArray, pList, pVector, pMatrix, pGraph, pMap, pSet). The STAPL PCF is distinguished from existing work by offering a class hierarchy and a composition mechanism which allows users to extend and customize the current container base for improved application expressivity and performance. We evaluate the performance of the STAPL pContainers on various parallel machines including a massively parallel CRAY XT4 system and an IBM P5-575 cluster. We show that the pContainer methods, generic pAlgorithms, and different applications, all provide good scalability on more than 10^4 processors