A Parallel Mesh-Adaptive Framework for Hyperbolic Conservation Laws

We report on the development of a computational framework for the parallel, mesh-adaptive solution of systems of hyperbolic conservation laws like the time-dependent Euler equations in compressible gas dynamics or Magneto-Hydrodynamics (MHD) and similar models in plasma physics. Local mesh refinement is realized by the recursive bisection of grid blocks along each spatial dimension, implemented numerical schemes include standard finite-differences as well as shock-capturing central schemes, both in connection with Runge-Kutta type integrators. Parallel execution is achieved through a configurable hybrid of POSIX-multi-threading and MPI-distribution with dynamic load balancing. One- two- and three-dimensional test computations for the Euler equations have been carried out and show good parallel scaling behavior. The Racoon framework is currently used to study the formation of singularities in plasmas and fluids.Comment: late submissio

Introduction of shared-memory parallelism in a distributed-memory multifrontal solver

We study the adaptation of a parallel distributed-memory solver towards a shared-memory code, targeting multi-core architectures. The advantage of adapting the code over a new design is to fully benefit from its numerical kernels, range of functionalities and internal features. Although the studied code is a direct solver for sparse systems of linear equations, the approaches described in this paper are general and could be useful to a wide range of applications. We show how existing parallel algorithms can be adapted to an OpenMP environment while, at the same time, also relying on third-party optimized multithreaded libraries. We propose simple approaches to take advantage of NUMA architectures, and original optimizations to limit thread synchronization costs. For each point, the performance gains are analyzed in detail on test problems from various application areas.Dans cet article, nous étudions l'adaptation d'un code parallèle à mémoire distribuée en un code visant les architectures à mémoire partagée de type multi-coeurs. L'intérêt d'adapter un code existant plutôt que d'en concevoir un nouveau est de pouvoir bénéficier directement de toute la richesse de ses fonctionnalités numériques ainsi que de ses caractéristiques internes. Même si le code sur lequel porte l'étude est un solveur direct multifrontale pour systèmes linéaires creux, les algorithmes et techniques discutés sont générales et peuvent s'appliquer à des domaines d'application plus généraux. Nous montrons comment des algorithmes parallèles existant peuvent être adaptés à un environnement OpenMP tout en exploitant au mieux des librairies existantes optimisées. Nous présentons des approches simples pour tirer parti des spécificités des architectures NUMA, ainsi que des optimisations originales permettant de limiter les coûts de synchronisation dans le modèle fork-join que l'on utilise. Pour chacun de ces points, les gains en performance sont analysés sur des cas tests provenant de domaines d'applications variés

Adaptive Parallelism for Coupled, Multithreaded Message-Passing Programs

Hybrid parallel programming models that combine message passing (MP) and shared- memory multithreading (MT) are becoming more popular, especially with applications requiring higher degrees of parallelism and scalability. Consequently, coupled parallel programs, those built via the integration of independently developed and optimized software libraries linked into a single application, increasingly comprise message-passing libraries with differing preferred degrees of threading, resulting in thread-level heterogeneity. Retroactively matching threading levels between independently developed and maintained libraries is difficult, and the challenge is exacerbated because contemporary middleware services provide only static scheduling policies over entire program executions, necessitating suboptimal, over-subscribed or under-subscribed, configurations. In coupled applications, a poorly configured component can lead to overall poor application performance, suboptimal resource utilization, and increased time-to-solution. So it is critical that each library executes in a manner consistent with its design and tuning for a particular system architecture and workload. Therefore, there is a need for techniques that address dynamic, conflicting configurations in coupled multithreaded message-passing (MT-MP) programs. Our thesis is that we can achieve significant performance improvements over static under-subscribed approaches through reconfigurable execution environments that consider compute phase parallelization strategies along with both hardware and software characteristics. In this work, we present new ways to structure, execute, and analyze coupled MT- MP programs. Our study begins with an examination of contemporary approaches used to accommodate thread-level heterogeneity in coupled MT-MP programs. Here we identify potential inefficiencies in how these programs are structured and executed in the high-performance computing domain. We then present and evaluate a novel approach for accommodating thread-level heterogeneity. Our approach enables full utilization of all available compute resources throughout an application’s execution by providing programmable facilities with modest overheads to dynamically reconfigure runtime environments for compute phases with differing threading factors and affinities. Our performance results show that for a majority of the tested scientific workloads our approach and corresponding open-source reference implementation render speedups greater than 50 % over the static under-subscribed baseline. Motivated by our examination of reconfigurable execution environments and their memory overhead, we also study the memory attribution problem: the inability to predict or evaluate during runtime where the available memory is used across the software stack comprising the application, reusable software libraries, and supporting runtime infrastructure. Specifically, dynamic adaptation requires runtime intervention, which by its nature introduces additional runtime and memory overhead. To better understand the latter, we propose and evaluate a new way to quantify component-level memory usage from unmodified binaries dynamically linked to a message-passing communication library. Our experimental results show that our approach and corresponding implementation accurately measure memory resource usage as a function of time, scale, communication workload, and software or hardware system architecture, clearly distinguishing between application and communication library usage at a per-process level

PARALLEL COMPUTING ALGORITHMS FOR TANDEM

Tandem mass spectrometry, also known as MS/MS, is an analytical technique to measure the mass-to-charge ratio of charged ions and widely used in genomics, proteomics and metabolomics areas. There are two types of automatic ways to interpret tandem mass spectra: de novo methods and database searching methods. Both of them need to use massive computational resources and complicated comparison algorithms. The real-time peptide-spectrum matching (RT-PSM) algorithm is a database searching method to interpret tandem mass spectra with strict time constraints. Restricted by the hardware and architecture of an individual workstation the RT-PSM algorithm has to sacrifice the level of accuracy in order to provide prerequisite processing speed. The peptide-spectrum similarity scoring module is the most time-consuming part out of four modules in the RT-PSM algorithm, which is also the core of the algorithm. In this study, a multi-core computing algorithm is developed for individual workstations. Moreover, a distributed computing algorithm is designed for a cluster. The improved algorithms can achieve the speed requirement of RT-PSM without sacrificing the accuracy. With some expansion, this distributed computing algorithm can also support different PSM algorithms. Simulation results show that compared with the original RT-PSM, the parallelization version achieves 25 to 34 times speed-up based on different individual workstations. A cluster with 240 CPU cores could accelerate the similarity score module 210 times compare with the single-thread similarity score module and the whole peptide identification process 85 times compare with the single-thread peptide identification process

Parallel Sparse Tensor Decomposition in Chapel

In big-data analytics, using tensor decomposition to extract patterns from large, sparse multivariate data is a popular technique. Many challenges exist for designing parallel, high performance tensor decomposition algorithms due to irregular data accesses and the growing size of tensors that are processed. There have been many efforts at implementing shared-memory algorithms for tensor decomposition, most of which have focused on the traditional C/C++ with OpenMP framework. However, Chapel is becoming an increasingly popular programing language due to its expressiveness and simplicity for writing scalable parallel programs. In this work, we port a state of the art C/OpenMP parallel sparse tensor decomposition tool, SPLATT, to Chapel. We present a performance study that investigates bottlenecks in our Chapel code and discusses approaches for improving its performance. Also, we discuss features in Chapel that would have been beneficial to our porting effort. We demonstrate that our Chapel code is competitive with the C/OpenMP code for both runtime and scalability, achieving 83%-96% performance of the original code and near linear scalability up to 32 cores.Comment: 2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), 5th Annual Chapel Implementers and Users Workshop (CHIUW 2018

GHOST: Building blocks for high performance sparse linear algebra on heterogeneous systems

While many of the architectural details of future exascale-class high performance computer systems are still a matter of intense research, there appears to be a general consensus that they will be strongly heterogeneous, featuring "standard" as well as "accelerated" resources. Today, such resources are available as multicore processors, graphics processing units (GPUs), and other accelerators such as the Intel Xeon Phi. Any software infrastructure that claims usefulness for such environments must be able to meet their inherent challenges: massive multi-level parallelism, topology, asynchronicity, and abstraction. The "General, Hybrid, and Optimized Sparse Toolkit" (GHOST) is a collection of building blocks that targets algorithms dealing with sparse matrix representations on current and future large-scale systems. It implements the "MPI+X" paradigm, has a pure C interface, and provides hybrid-parallel numerical kernels, intelligent resource management, and truly heterogeneous parallelism for multicore CPUs, Nvidia GPUs, and the Intel Xeon Phi. We describe the details of its design with respect to the challenges posed by modern heterogeneous supercomputers and recent algorithmic developments. Implementation details which are indispensable for achieving high efficiency are pointed out and their necessity is justified by performance measurements or predictions based on performance models. The library code and several applications are available as open source. We also provide instructions on how to make use of GHOST in existing software packages, together with a case study which demonstrates the applicability and performance of GHOST as a component within a larger software stack.Comment: 32 pages, 11 figure

Design and Implementation of an Agent-Based Model of Pertussis with Performance Considerations

Pertussis, also known as Whooping Cough, is an airborne communicable disease caused by the Bordetella pertussis bacterium. Symptoms include fever, runny nose, and a cough that typically progresses to the point where it interferes with breathing, producing the characteristic whoop from which the common name is derived. Complications, which disproportionately affect infants, include bacterial pneumonia which can lead to death. Pertussis is vaccine-preventable and vaccination programs exist in most countries yet a recent resurgence has been observed in jurisdictions with high vaccine coverage, including Alberta and Canada. Simulation modeling has a long history in the study of epidemiology, including that of pertussis, but most of such work has employed compartmental models. Agent-based models (ABMs) allow differentiation down to the individual level, which cannot be done in aggregate compartmental models, as well as simpler specification of heterogeneity and interaction patterns which can be tedious to implement in aggregate compartmental models. These benefits come at the cost of increased computational burden. This thesis seeks to design and implement an ABM representing the epidemiology of pertussis in Alberta, Canada, and apply that model to evaluate vaccination during pregnancy as a potential intervention strategy to reduce pertussis incidence in infants. In support of this objective, data structures will be explored to improve performance for large ABMs developed using AnyLogic software