The efficient solution of sparse, linear systems that arise through the discretization of partial differential equations remains a key challenge for a range of high performance scientific simulations. One approach for reducing data movement and improving performance is by exposing and exploiting structure in a problem through the use of robust structured multilevel solvers. By choosing coarsening that

A standard approach for computing eigenvectors of a non-symmetric matrix reduced to real Schur form relies on a variant of backward substitution. Backward substitution is prone to overflow. To avoid overflow, the LAPACK eigenvector routine DTREVC3 associates every eigenvector with a scaling factor and dynamically rescales an entire eigenvector during the backward substitution such that overflow cannot

Training a Deep Neural Network (DNN) is a significant computing task since it places high demands on computing resources and memory bandwidth. Many approaches have been proposed to compress the network, while maintaining high model accuracy, reducing the computational demands associated with large-scale DNN training. One attractive approach is to leverage Block Circulant Matrices (BCM), compressing

Using the Löwdin orthonormalization of tall-skinny matrices as a proxy-app for wavefunction-based Density Functional Theory solvers, we investigate a distributed memory parallel strategy focusing on Graphics Processing Unit (GPU)-accelerated nodes as available on some of the top ranked supercomputers at the present time. We present numerical results in the strong limit regime, as it is particularly

Spark Streaming is an extension of the core Spark engine that enables scalable, high-throughput, fault-tolerant stream processing of live data streams. It treats stream as a series of deterministic batches and handles them as regular jobs. However, for a stream job responsible for a batch, data skew (i.e., the imbalance in the amount of data allocated to each reduce task), can degrade the job performance

The energy efficiency of a data center depends on the cooling cost of clusters in the data center. Enhancing thermal efficiency of clusters is a practical approach to reducing energy consumption cost, optimizing scalability, and improving reliability. In this paper, we propose ThermoBench to evaluate the thermal efficiency of computing and storage clusters deployed in data centers. We shed light on

Because of the importance of Delaunay Triangulation in science and engineering, researchers have devoted extensive attention to parallelizing this fundamental algorithm. However, generating unstructured meshes for extremely large point sets remains a barrier for scientists working with large scale or high resolution datasets. In our previous paper, we introduced a novel algorithm – Triangulation of

We propose a two-stage optimization strategy to accelerate non-equilibrium ionization (NEI) calculation that is crucial to various high energy astrophysical phenomena, by using methods of MapReduce modeling and GPU acceleration. First, we construct a parallel pipeline based on the MapReduce model that processes massive particles trajectories on a separate mesh decoupled from that has been used by other

High performance computing (HPC) systems are confronting the challenge of improving their productivity under a system-wide power constraint in the exascale era. To measure the productivity of an HPC job, researchers have proposed to assign a monotonically decreasing time-dependent value function, called job-value, to that job. These job-value functions are used by the value-based scheduling algorithms

The use of hybrid scheme combining the message passing programming models for inter-node parallelism and the shared memory programming models for node-level parallelism is widely spread. Existing extensive practices on hybrid Message Passing Interface (MPI) plus Open Multi-Processing (OpenMP) programming account for its popularity. Nevertheless, strong programming efforts are required to gain performance

The Sunway TaihuLight with the theoretical peak performance of 125PFlop/s is now ranked third in the TOP500 list. It provides a high-level programming model named OpenACC, which extends the OpenACC 2.0 standard with some customized extensions. We assess the performance of the extended programming model and the SW26010 heterogeneous many-core processor for running molecular dynamics (MD) simulation

In this paper, we describe a GPU-accelerated parallel algorithm for the axial Multidimensional Assignment Problem with Decomposable Costs (MDADC), which is one of the most fundamental formulations for data association. MDADC is known to be NP-hard and is large-dimensioned in most realistic cases; hence, heuristic solutions with qualified optimality gaps is the best one can hope for, given the state-of-knowledge

Parallel task-based programming models, like OpenMP, allow application developers to easily create a parallel version of their sequential codes. The standard OpenMP 4.0 introduced the possibility of describing a set of data dependences per task that the runtime uses to order the tasks execution. This order is calculated using shared graphs, which are updated by all threads in exclusive access using

Genetic algorithms can be employed to automatically generate desired test data, with the advantage of freeing up manpower. For the path coverage criterion, the problem of test data generation needs to be transformed into an optimization problem before applying genetic algorithms. However, when the number of paths to be covered is large, the transformed optimization problem will be very complicated

Optimization problems have great importance in the industrial field, specially for supply chain management and transportation of goods. Many of these problems are classified as NP-Hard, thus there is no known algorithm to find their exact (global optimal) solutions in polynomial time. Therefore, fast heuristic strategies are generally employed, specially those with the ability to escape from poor quality

The increased degree of concurrent operations by lower precision arithmetic enables high performance for iterative refinement. Most of related work present statically defined mixed precision arithmetic approaches, while adapting a level of arithmetic precision dynamically in a loop with one-bit granularity can further improve the performance. This paper presents Arbitrary Dynamic Precision Iterative

In distributed implementations of memory-bound stencil AMR applications, the inter-node communication time often represents a major performance bottleneck. Thus minimizing communication is an objective as important as maintaining a good load balance. We propose a new domain partitioning method for block-based AMR applications based on the multi-phase-field (MPF) model. The MPF model for polycrystalline

The tremendous challenge of developing applications efficiently utilizing the hardware provided by contemporary parallel systems of all scales is among the most limiting factors for the continuous growth of high performance computing. In this article, we present a novel architecture taking on this challenge by providing an infrastructure for the effective development of such applications. Our design

As modern HPC applications and systems advance to exascale, their complexity and the need for more efficient resource utilization increases. This fact demands more advanced monitoring, analysis and optimization approaches. Therefore, the Message Passing Interface (MPI), which is the most common parallel programming system for HPC applications, must enable these advanced approaches. Even if the existing

We investigate sparse matrix bipartitioning – a problem where we minimize the communication volume in parallel sparse matrix-vector multiplication. We prove, by reduction from graph bisection, that this problem is NP-complete in the case where each side of the bipartitioning must contain a linear fraction of the nonzeros. We present an improved exact branch-and-bound algorithm which finds the minimum

Triggered operations and counting events or counters are building blocks used by communication libraries, such as MPI, to offload collective operations to the Host Fabric Interface (HFI) or Network Interface Card (NIC). Triggered operations can be used to schedule a network or arithmetic operation to occur in the future, when a trigger counter reaches a specified threshold. On completion of the operation

We present a high-performance solver for dense skew-symmetric matrix eigenvalue problems. Our work is motivated by applications in computational quantum physics, where one solution approach to solve the Bethe-Salpeter equation involves the solution of a large, dense, skew-symmetric eigenvalue problem. The computed eigenpairs can be used to compute the optical absorption spectrum of molecules and crystalline

Global data movement is the most general, and therefore important, function of inter-node communication in the partitioned global address space programming models, such as XcalableMP. Our implementation of it consists of compile-time and run-time optimization for specific cases and run-time processing based on the calculus of common-stride section descriptors for general cases, which allows efficient

Communication exacerbates the performance for parallel applications with thousands of CPU cores and quantities of data to exchange. The high communication cost is usually attributed to the mismatch between the communication patterns of parallel applications and the physical topology graphs of the computing resources (or the underlying network topologies). The topology-aware process mapping method can

New regulations are imposing noise emissions limitations for the aviation industry which are pushing researchers and engineers to invest efforts in studying the aeroacoustics phenomena. Following this trend, an in-house computational fluid dynamics tool is build to reproduce high fidelity results of supersonic jet flows for aeroacoustic analogy applications. The solver is written using the large eddy

This paper deals with scalability improvements of the FETI (Finite Element Tearing and Interconnecting) domain decomposition method solving elliptic PDEs. The main bottleneck of FETI is the solution of a coarse problem that is part of the projector onto the natural coarse space. This paper introduces and compares two strategies for the FETI coarse problem solution. The first one is a classical solution

We present a scalable incomplete LU factorization to be used as a preconditioner for solving sparse linear systems with iterative methods in the package called Javelin. Javelin allows for improved parallel factorization on shared-memory many-core systems by packaging the coefficient matrix into a format that allows for high performance sparse matrix-vector multiplication and sparse triangular solves

Encryption plays an important role in protecting data, especially data transferred on the Internet. However, encryption is computationally expensive and this leads to high energy costs. Parallel encryption solutions using more CPU/GPU cores can achieve high performance. If we consider energy efficiency to be cost effective using parallel encryption solutions at the same time, this problem can be alleviated

This work presents recent advances in visualizing multi-physics, fluid-structure interaction (FSI) phenomena in cerebral aneurysms. Realistic FSI simulations produce very large and complex data sets, yielding the need for parallel data processing and visualization. Here we present our efforts to develop an interactive visualization tool which enables the visualization of such FSI simulation data. Specifically

The cellular process responsible for providing energy for most life on Earth, namely photosynthetic light-harvesting, requires the cooperation of hundreds of proteins across an organelle, involving length and time scales spanning several orders of magnitude over quantum and classical regimes. Simulation and visualization of this fundamental energy conversion process pose many unique methodological

This paper presents a parallel simulated annealing algorithm that is able to achieve 90% parallel efficiency in iteration on up to 192 processors and up to 40% parallel efficiency in time when applied to a 5000-dimension Rastrigin function. Our algorithm breaks scalability barriers in the method of Chu et al. (1999) by abandoning adaptive cooling based on variance. The resulting gains in parallel efficiency

We introduce a region template abstraction and framework for the efficient storage, management and processing of common data types in analysis of large datasets of high resolution images on clusters of hybrid computing nodes. The region template abstraction provides a generic container template for common data structures, such as points, arrays, regions, and object sets, within a spatial and temporal

Simulation of in vivo cellular processes with the reaction-diffusion master equation (RDME) is a computationally expensive task. Our previous software enabled simulation of inhomogeneous biochemical systems for small bacteria over long time scales using the MPD-RDME method on a single GPU. Simulations of larger eukaryotic systems exceed the on-board memory capacity of individual GPUs, and long time

We address the problem of efficient execution of a computation pattern, referred to here as the irregular wavefront propagation pattern (IWPP), on hybrid systems with multiple CPUs and GPUs. The IWPP is common in several image processing operations. In the IWPP, data elements in the wavefront propagate waves to their neighboring elements on a grid if a propagation condition is satisfied. Elements receiving

Elementary mode analysis is a useful metabolic pathway analysis tool in understanding and analyzing cellular metabolism, since elementary modes can represent metabolic pathways with unique and minimal sets of enzyme-catalyzed reactions of a metabolic network under steady state conditions. However, computation of the elementary modes of a genome- scale metabolic network with 100-1000 reactions is very

The graphics processing unit (GPU) is used to solve large linear systems derived from partial differential equations. The differential equations studied are strongly convection-dominated, of various sizes, and common to many fields, including computational fluid dynamics, heat transfer, and structural mechanics. The paper presents comparisons between GPU and CPU implementations of several well-known

Physical and engineering practicalities involved in microprocessor design have resulted in flat performance growth for traditional single-core microprocessors. The urgent need for continuing increases in the performance of scientific applications requires the use of many-core processors and accelerators such as graphics processing units (GPUs). This paper discusses GPU acceleration of the multilevel

MUMmerGPU uses highly-parallel commodity graphics processing units (GPU) to accelerate the data-intensive computation of aligning next generation DNA sequence data to a reference sequence for use in diverse applications such as disease genotyping and personal genomics. MUMmerGPU 2.0 features a new stackless depth-first-search print kernel and is 13× faster than the serial CPU version of the alignment

As ribonucleic acid (RNA) molecules play important roles in many biological processes including gene expression and regulation, their secondary structures have been the focus of many recent studies. Despite the computing power of supercomputers, computationally predicting secondary structures with thermodynamic methods is still not feasible when the RNA molecules have long nucleotide sequences and

FPGA-based acceleration of molecular dynamics simulations (MD) has been the subject of several recent studies. The short-range force computation, which dominates the execution time, is the primary focus. Here we combine: a high level of FPGA-specific design including cell lists, systematically determined interpolation and precision, handling of exclusion, and support for MD simulations of up to 256K

Approximate string matching is fundamental to bioinformatics and has been the subject of numerous FPGA acceleration studies. We address issues with respect to FPGA implementations of both BLAST- and dynamic-programming- (DP) based methods. Our primary contribution is a new algorithm for emulating the seeding and extension phases of BLAST. This operates in a single pass through a database at streaming