Abstract. Myrinet is a new type of local-area network (LAN) based on the technology used for packet communication and switching within "massivelyparallel processors " (MPPs). Think of Myrinet as an MPP message-passing network that can span campus dimensions, rather than as a wide

sufficiently large number of dislocation lines, in order to be statistically representative and to reproduce experimentally observed microstructures. A new massively-parallel DD code is developed that is capable of modeling million-dislocation systems by employing thousands of processors. We dis

of biological molecular networks that leverage the massively-parallel computing power of modern graphics cards and other many-core programming paradigms. Our system can automatically compile standard SBML files into CUDA code, using analytic derivatives, and computing standard measures of complex dynamics like

The SpiNNaker project aims to develop parallel computer systems with more than a million embedded processors. The goal of the project is to support large-scale simulations of systems of spiking neurons in biological real time, an application that is highly parallel but also places very high loads

A novel approach to parallelize the well-known Hoshen-Kopelman algorithm has been chosen, suitable for simulating huge lattices in high dimensions on massively-parallel computers with distributed memory and message passing. This method consists of domain decomposition of the simulated lattice

Abstract—Statistical debugging identifies program behaviors that are highly correlated with failures. Traditionally, this ap-proach has been applied to desktop software on which it is effective in identifying the causes that underlie several difficult classes of bugs including: memory corruption, non-deterministic bugs, and bugs with multiple temporally-distant triggers. The domain of scientific computing offers a new target for this type of debugging. Scientific code is run at massive scales offering massive quantities of statistical feedback data. Data collection can scale well because it requires no communication between compute nodes. Unfortunately, existing statistical debugging techniques impose run-time overhead that is unsuitable for computationally-intensive code despite being modest and acceptable in desktop software. Additionally, the normal communication that occurs between nodes in parallel jobs violates a key assumption of statistical independence in existing statistical models. We report on our experience bringing statistical debugging to the domain of scientific computing. We present techniques to reduce the run-time overhead of the required instrumentation by up to 25 % over prior work, along with challenges related to data collection. We also discuss case studies looking at real bugs in ParaDiS and BOUT++, as well as some manually-seeded bugs. We demonstrate that the loss of statistical independence between runs is not a problem in practice. I.

Abstract—We studied neuromorphic models of binocular dis-parity processing and mapped them onto a vision chip containing a massively parallel analog processor array. Our goal was to make efficient use of the available hardware while preserving the fundamental computations performed by the models

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Abstract—SpiNNaker is a biologically-inspired massively-parallel computer design that will contain over a million pro-cessors, distributed over more than 60,000 chips. The system bootstrap must discover how they are connected for the machine to enter a usable state. In this paper we describe a set

We present the architecture of Triton/i, a scalable, mixed-mode (SIMD/MIMD) parallel computer. The novel features of Triton/1 are: Support for high-level, machine-independent programming languages; Fast SlrMD/MlrMD mode switching; Special hardware for barrier synchronization of multiple process groups; A self-routing, dead-lock-free perfect shuffle interconnect with latency hiding. The