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Dedicated network connections are being increasingly deployed in cloud, centralized and edge computing and data infrastructures, whose throughput profiles are critical indicators of the underlying data transfer performance. Due to the cost and ...

Novel uses of graphical processing units for accelerated computation revolutionized the field of high-performance scientific computing by providing specialized workflows tailored to algorithmic requirements. As the era of Moore’s law draws to a close, ...

Scientific computations are expected to be increasingly distributed across wide-area networks, and Message Passing Interface (MPI) has been shown to scale to support their communications over long distances. Application-level measurements of MPI ...

We develop a machine learning (ML) based design space exploration (DSE) method that builds predictive models for various responses of a hybrid main-memory system. To overcome the challenges associated with latency, capacity, and power of memory systems ...

• Heuristics were designed for maximizing the utility earned by parallel tasks.
• ...

The worth of completing parallel tasks is modeled using utility functions, which monotonically-decrease with time and represent the importance and urgency of a task. These functions define the utility earned by a task at the time of ...

We consider high performance computing infrastructures consisting of multiple sites connected over a wide-area network. These sites house heterogeneous computing systems, network elements and local-area connections, and the wide-area network plays a ...

We study the applicability of spiking neural networks and neuromorphic hardware for solving general opti- mization problems without the use of adaptive training or learning algorithms. We leverage the dynamics of Hopfield networks and spin-glass systems ...

State-of-the-art synchronous graph processing frameworks face both inefficiency and imbalance issues that cause their performance to be suboptimal. These issues include the inefficiency of communication and the imbalanced graph computation/communication ...

We present results related to the performance of an algorithm for community detection which incorporates event-driven computation. We define a mapping which takes a graph 𝒢 to a system of symmetrically connected, spiking neurons and use spike train ...

Recently, there has been a growing interest in enabling fast data analytics by leveraging system capabilities from large-scale high-performance computing (HPC) systems. OpenSHMEM is a popular run-time system on HPC systems that has been used for large-...

Modern graphics processing units (GPUs) have complex architectures that admit exceptional performance and energy efficiency for high-throughput applications. Although GPUs consume large amounts of power, their use for high-throughput applications ...

This document describes the effort to implement the Graph 500 benchmark using OpenSHMEM based on the MPI-2 one-side version. The Graph 500 benchmark performs a breadth-first search in parallel on a large randomly generated undirected graph and can be ...