Abstract
Recently, computing platforms have been being configured on a large scale to satisfy the diverse requirements of emerging applications like big data and graph processing, neural network, speech recognition and so on. In these computing platforms, each computing node consists of a multicore, an accelerator, and a complex memory hierarchy, which are connected to other nodes using a variety of high-performance networks. Up to now, researchers have been using cycle-accurate simulators to evaluate the performance of computer systems in detail. However, the execution of the simulators, which models modern computing architecture for multi-core, multi-node, datacenter, memory hierarchy, new memory, and new interconnection, is too slow and infeasible; since the architecture has become more complex today, the complexity of the simulator is rapidly increasing. Therefore, it is seriously challenging to employ them in the research and development of next-generation computer systems. To solve this problem, we previously presented EPSim (Epoch-based Simulator), which defines epochs that can be run independently by dividing the simulation run into several sections and executes them in parallel on a multicore platform, resulting in only the limited simulation speedup. In this paper, to overcome the computing resource limitations on multi-core platforms, we propose a novel EPSim-C (EPSim on Cloud) simulator that extends EPSim and achieves higher performance using a cloud computing platform. EPSim-C is designed to perform the epoch-based executions in a massively parallel fashion by using MapReduce on Hadoop-based systems. According to our experiments, we have achieved a maximum speed of 87.0× and an average speed of 46.1× using 256 cores. As far as we know, EPSim-C is the only existing way to accelerate the cycle-accurate simulator on cloud platforms; thus, our significant performance enhancement allows researchers to model and research current and future cutting-edge computing platforms using real workloads.
Highlights
As the demand for big data analysis and neural network processing has increased explosively, the use of data-intensive applications has drastically increased [1,2].Because data-intensive processing grows linearly in execution time with data size and has data-level parallelism, its performance can be effectively improved through parallelization [3,4]
There have been various approaches to accelerate the applications by exploiting data-level parallelism using immense amounts of hardware resources, such as in cloud and neural computing platforms [5,6,7] which are comprised of a variety of state-of-the-art multiple CPUs and GPUs, large-scale memory, high-speed network connections, etc. [8,9,10,11]
We explore the parallelism of EPSim and accelerate its simulation significantly using cloud computing to exploit the massive parallelism
Summary
As the demand for big data analysis and neural network processing has increased explosively, the use of data-intensive applications has drastically increased [1,2].Because data-intensive processing grows linearly in execution time with data size and has data-level parallelism, its performance can be effectively improved through parallelization [3,4]. As the demand for big data analysis and neural network processing has increased explosively, the use of data-intensive applications has drastically increased [1,2]. There have been various approaches to accelerate the applications by exploiting data-level parallelism using immense amounts of hardware resources, such as in cloud and neural computing platforms [5,6,7] which are comprised of a variety of state-of-the-art multiple CPUs and GPUs, large-scale memory, high-speed network connections, etc. We present our previous multi-core based parallel EPSim simulator derived from MARSSx86. The cloud is an on-demand computing platform that provides shared computing resources, such as servers, storage, networks, applications, and services, via the Internet. The cloud clients can access cloud services by using a web browser, mobile applications, thin clients, terminal emulators, and so on over the Internet
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