A Simulator for Large-Scale Parallel Computer Architectures

Janssen, Curtis L.; Adalsteinsson, Helgi; Cranford, Scott; Kenny, Joseph P.; Pınar, Ali; Evensky, David A.; Mayo, Jackson

doi:10.4018/978-1-4666-0906-8.ch011

Cited by 24 publications

(40 citation statements)

References 9 publications

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“…For the sake of flexibility, LWSim is not a parallel simulator, and therefore has scaling limits. For full scaling, we are working towards integrating our efforts with those achieved by the Sandia National Laboratories with SSTMacro [24]. We are also working towards closer integration with third party tools.…”

Section: Toward the Phoenixsim Suitementioning

confidence: 99%

“…We therefore need modeling and simulation tools to understand the interactions between real time applications and the hardware [24]. More specifically, we need to compare the various possible associations of a data-plane (topology, mix of electrical or optical components) with one or more resource allocation schemes (centralized compared to distributed, synchronous compared to.…”

Section: Introductionmentioning

confidence: 99%

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Modeling and simulation environment for photonic interconnection networks in high performance computing

Glick

Rumley

Hendry

et al. 2013

2013 15th International Conference on Transparent Optical Networks (ICTON)

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Computer network architects are concerned with reducing the execution time of applications while maintaining or reducing power consumption of the system. In order to demonstrate the advantages of photonic interconnection networks at the scale of high performance computer clusters and warehouse scale data centers, system level simulations are required. To achieve an appropriate level of accuracy both for device detail and system scale, a simulation suite, rather than a single simulation tool will be most efficient. We present our work on this topic and results for rack scale photonic interconnection networks for high performance computing. Keywords: optical networks, optical interconnects, simulation. INTRODUCTION AND MOTIVATIONSolutions are required to the newsworthy power consumption [3], latency [4] and bandwidth challenges [5] of high performance computing (HPC). Low latency, previously primarily viewed as a requirement for supercomputers, is becoming a more significant metric as the data center size increases to warehouse scale and web search results gather information from more servers [6,7]: beyond a given scale, the parallel efficiency (and thus the general efficiency) is directly related to the latency.Silicon photonics (SiP) [1,2] has the potential to address these challenges and offer improved network functionality. SiP based device capabilities and manufacturability are advancing rapidly in several key areas including: ring resonator temperature stabilization [8], integrated switch fabrics [9,10] and hybrid silicon technology [11]. Integrated silicon (and InP based) photonics reduce power consumption and cost in addition to providing high speed switching capabilities. With off chip bandwidths reaching 100's GB/s [12] high bandwidth photonics is clearly an option to reduce I/O bandwidth bottlenecks. However, proof of concept demonstrations at the device or link level are not sufficient to convince computer architects to incorporate photonics into their next generation system designs. On the other hand, system experiments from computer architects tend to focus on commercially available products, whose price and specifications can easily be obtained [1,2]. Enabling interaction between SiP device designers and computer architects is necessary for realizing novel systems with integrated silicon photonics. Consequently, we must take the extra steps to demonstrate, through modeling and simulations (and, where possible, test-bed validations), the effects of incorporating photonic technology in computer networks. This means, in addition to showing the impact on network metrics such as latency and bandwidth, developing tools that illustrate the execution time improvement of target applications.The optimal way to incorporate SiP into a large scale system depends on the communication requirements of the executed applications. We therefore need modeling and simulation tools to understand the interactions between real time applications and the hardware [24]. More specifically, we need to compare the various...

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Section: Toward the Phoenixsim Suitementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling and simulation environment for photonic interconnection networks in high performance computing

Glick

Rumley

Hendry

et al. 2013

2013 15th International Conference on Transparent Optical Networks (ICTON)

View full text Add to dashboard Cite

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“…The Extreme-scale Simulator (xSim) is a performance investigation toolkit for running MPI applications at extreme scale by running on existing machines in a heavily oversubscribed mode [3]. The Sandia SST/Macro [8] project has similar goals but employs a different approach. Both of these performance tools employ parallel discrete simulation to study the effects of different hardware characteristics on application performance as the systems scale up in size.…”

Section: Related Workmentioning

confidence: 99%

“…The library records both the input arguments and return values 1 for the MPI functions. The library can also support tracing individual functions, and can be configured to record performance counter data using PAPI [8]. There are utilities for converting DUMPI trace files to Open Trace Format (OTF) files, which could be used with existing performance visualization tools like Vampir.…”

Section: Requirements and Approachmentioning

confidence: 99%

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Using Performance Tools to Support Experiments in HPC Resilience

Naughton

Bohm

Engelmann

et al. 2014

Euro-Par 2013: Parallel Processing Workshops

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Abstract. The high performance computing (HPC) community is working to address fault tolerance and resilience concerns for current and future large scale computing platforms. This is driving enhancements in the programming environments, specifically research on enhancing message passing libraries to support fault tolerant computing capabilities. The community has also recognized that tools for resilience experimentation are greatly lacking. However, we argue that there are several parallels between "performance tools" and "resilience tools". As such, we believe the rich set of HPC performance-focused tools can be extended (repurposed) to benefit the resilience community. In this paper, we describe the initial motivation to leverage standard HPC performance analysis techniques to aid in developing diagnostic tools to assist fault tolerance experiments for HPC applications. These diagnosis procedures help to provide context for the system when the errors (failures) occurred. We describe our initial work in leveraging an MPI performance trace tool to assist in providing global context during fault injection experiments. Such tools will assist the HPC resilience community as they extend existing and new application codes to support fault tolerances.

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Performance modeling of a geophysics application to accelerate over‐decomposition parameter tuning through simulation

Tesser

Schnorr

Legrand

et al. 2018

Concurrency and Computation

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Summary Finite‐difference methods are commonplace in High Performance Computing applications. Despite their apparent regularity, they often exhibit load imbalance that damages their efficiency. We characterize the spatial and temporal load imbalance of Ondes3D, a typical finite‐differences application dedicated to earthquake modeling. Our analysis reveals imbalance originating from the structure of the input data, and from low‐level CPU optimizations. Ondes3D was successfully ported to AMPI/CHARM++ using over‐decomposition and MPI process migration techniques to dynamically rebalance the load. However, this approach requires careful selection of the over‐decomposition level, the load balancing algorithm, and its activation frequency. These choices are usually tied to application structure and platform characteristics. In this article, we propose a workflow that leverages the capabilities of SimGrid to conduct such study at low experimental cost. We rely on a combination of emulation, simulation, and application modeling that requires minimal code modification and manages to capture both spatial and temporal load imbalance to faithfully predict the performance of dynamic load balancing. We evaluate the quality of our simulation by comparing simulation results with the outcome of real executions and demonstrate how this approach can be used to quickly find the optimal load balancing configuration for a given application/hardware configuration.

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A Simulator for Large-Scale Parallel Computer Architectures

Cited by 24 publications

References 9 publications

Modeling and simulation environment for photonic interconnection networks in high performance computing

Modeling and simulation environment for photonic interconnection networks in high performance computing

Using Performance Tools to Support Experiments in HPC Resilience

Performance modeling of a geophysics application to accelerate over‐decomposition parameter tuning through simulation

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