HAEC-SIM: A Simulation Framework for Highly Adaptive Energy-Efficient Computing Platforms

Bielert, Mario; Ciorba, Florina M.; Feldhoff, Kim; Ilsche, Thomas; Nagel, Wolfgang E.

doi:10.4108/eai.24-8-2015.2261105

Cited by 5 publications

(7 citation statements)

References 26 publications

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“…Whenever it is not possible to conduct experiments on a platform, the MPI runtime can be simulated and some power models can help characterize the energy consumption. In Reference 35 the authors propose a simulator for several programming paradigms including MPI. It uses an input trace generated from the instrumented code of the application and outputs the total energy consumption using an energy model adapted for each computing node.…”

Section: Related Workmentioning

confidence: 99%

Adaptive message passing polling for energy efficiency: Application to software‐distributed shared memory over heterogeneous computing resources

Cudennec

2020

Concurrency and Computation

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Autonomous vehicles, smart manufacturing, heterogeneous systems, and new high-performance embedded computing (HPEC) applications can benefit from the reuse of code coming from the high-performance computing world. However, unlike for HPC, energy efficiency is critical in embedded systems, especially when running on battery power. Code base from HPC mostly relies on the message passing interface (MPI) message passing runtime to deal with distributed systems. MPI has been designed primarily for performance and not for energy efficiency. One drawback is the way messages are received, in an energy-consuming busy-wait fashion. In this work, we study a simple approach in which receiving processes are put to sleep instead of constantly polling. We implement this strategy at the user level to be transparent to the MPI runtime and the application. Experiments are conducted with OpenMPI, MPICH, and MPC, using a video processing application and a software-distributed shared memory system deployed over two heterogeneous platforms, including the Christmann RECS|Box Antares Microserver. Results show significant energy savings. In some particular cases involving process colocation, we also observe better performance using our strategy which can be explained by a better sharing of the computing resource.

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Section: Related Workmentioning

confidence: 99%

Adaptive message passing polling for energy efficiency: Application to software‐distributed shared memory over heterogeneous computing resources

Cudennec

2020

Concurrency and Computation

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“…In this work, we employ a contention-oblivious communication model called network coding dynamic resilient (NCD r ) [43] that transmits messages efficiently, reliably, and with minimal energy costs. NCD r is implemented in HAEC-SIM [16], the trace-based simulator used in this work.…”

Section: Related Workmentioning

confidence: 99%

“…To model communication, we use a contention-oblivious implementation of the NCD r network model [44], implemented in HAEC-SIM [16]. This model describes the separation of message bodies into packets and the duration of efficient, reliable, minimal energy cost transmissions over the network based on future-generation wireless and optical links.…”

Section: Communication Modelmentioning

confidence: 99%

Mapping Matters: Application Process Mapping on 3-D Processor Topologies

Korndörfer,

Bielert,

Pilla

et al. 2020

Preprint

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Applications' performance is influenced by the mapping of processes to computing nodes, the frequency and volume of exchanges among processing elements, the network capacity, and the routing protocol. A poor mapping of application processes degrades performance and wastes resources. Process mapping is frequently ignored as an explicit optimization step since the system typically offers a default mapping, users may lack awareness of their applications' communication behavior, and the opportunities for improving performance through mapping are often unclear. This work studies the impact of application process mapping on several processor topologies. We propose a workflow that renders mapping as an explicit optimization step for parallel applications. We apply the workflow to a set of four applications, twelve mapping algorithms, and three direct network topologies. We assess the mappings' quality in terms of volume, frequency, and distance of exchanges using metrics such as dilation (measured in hop•Byte). With a parallel trace-based simulator, we predict the applications' execution on the three topologies using the twelve mappings. We evaluate the impact of process mapping on the applications' simulated performance in terms of execution and communication times and identify the mappings that achieve the highest performance in

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“…HAEC-SIM 1 [3] is an integrated simulation environment designed for the study of the performance and energy costs of the HAEC Box running energy-aware applications.…”

Section: Motivationmentioning

confidence: 99%

“…Previous simulations were focussed on the future platform specification described in [3]. However, in this work, parameters that characterize networks in current large-scale high performance computing (HPC) systems are used.…”

Section: Simulated System Specificationsmentioning

confidence: 99%

Simulation Models Verification for Resilient Communication on a Highly Adaptive Energy-Efficient Computer

Pfennig

Feldhoff

Ciorba

et al. 2016

24th High Performance Computing Symposium

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The utility of simulations depends on the confidence in the simulation implementation and its results. This study discusses the verification of the communication models in the parallel trace-driven simulation framework HAEC-SIM. As simulation input, a parallel application is executed and recorded on an existing HPC system. The simulation focuses on modeling the transfer times of point-to-point messages within the application and the indirect effects resulting in an output trace of application events of the simulated execution on a target platform. Consequently, via verification the message transfer times obtained with HAEC-SIM are compared with those of an independent implementation of the communication models. Both implementations consider the number of hops, the size and the target system parameters for each message. During verification the following factors are varied: application benchmarks, network topologies, mapping strategies, and resilient communication models.Verification yields an almost perfect agreement: the transfer times differ for a tiny percentage (0.00019 %) of messages by a negligible deviation of one picosecond, which is the finest granularity of the time data type. This result strengthens the confidence in a correct implementation of the communication models in simulation.

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HAEC-SIM: A Simulation Framework for Highly Adaptive Energy-Efficient Computing Platforms

Cited by 5 publications

References 26 publications

Adaptive message passing polling for energy efficiency: Application to software‐distributed shared memory over heterogeneous computing resources

Adaptive message passing polling for energy efficiency: Application to software‐distributed shared memory over heterogeneous computing resources

Mapping Matters: Application Process Mapping on 3-D Processor Topologies

Simulation Models Verification for Resilient Communication on a Highly Adaptive Energy-Efficient Computer

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