Proceedings of the 18th ACM International Conference on Computing Frontiers最新文献_第4页

On the role of system software in energy management of neuromorphic computing 论系统软件在神经形态计算能量管理中的作用

Proceedings of the 18th ACM International Conference on Computing Frontiers Pub Date : 2021-03-22 DOI: 10.1145/3457388.3458664

Twisha Titirsha, Shihao Song, Adarsha Balaji, Anup Das

{"title":"On the role of system software in energy management of neuromorphic computing","authors":"Twisha Titirsha, Shihao Song, Adarsha Balaji, Anup Das","doi":"10.1145/3457388.3458664","DOIUrl":"https://doi.org/10.1145/3457388.3458664","url":null,"abstract":"Neuromorphic computing systems such as DYNAPs and Loihi have recently been introduced to the computing community to improve performance and energy efficiency of machine learning programs, especially those that are implemented using Spiking Neural Network (SNN). The role of a system software for neuromorphic systems is to cluster a large machine learning model (e.g., with many neurons and synapses) and map these clusters to the computing resources of the hardware. In this work, we formulate the energy consumption of a neuromorphic hardware, considering the power consumed by neurons and synapses, and the energy consumed in communicating spikes on the interconnect. Based on such formulation, we first evaluate the role of a system software in managing the energy consumption of neuromorphic systems. Next, we formulate a simple heuristic-based mapping approach to place the neurons and synapses onto the computing resources to reduce energy consumption. We evaluate our approach with 10 machine learning applications and demonstrate that the proposed mapping approach leads to a significant reduction of energy consumption of neuromorphic computing systems.","PeriodicalId":136482,"journal":{"name":"Proceedings of the 18th ACM International Conference on Computing Frontiers","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114944058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 22

Reducing quantum annealing biases for solving the graph partitioning problem 减少量子退火偏差解决图划分问题

Proceedings of the 18th ACM International Conference on Computing Frontiers Pub Date : 2021-03-08 DOI: 10.1145/3457388.3458672

Elijah Pelofske, Georg Hahn, H. Djidjev

{"title":"Reducing quantum annealing biases for solving the graph partitioning problem","authors":"Elijah Pelofske, Georg Hahn, H. Djidjev","doi":"10.1145/3457388.3458672","DOIUrl":"https://doi.org/10.1145/3457388.3458672","url":null,"abstract":"Quantum annealers offer an efficient way to compute high quality solutions of NP-hard problems when expressed in a QUBO (quadratic unconstrained binary optimization) or an Ising form. This is done by mapping a problem onto the physical qubits and couplers of the quantum chip, from which a solution is read after a process called quantum annealing. However, this process is subject to multiple sources of biases, including poor calibration, leakage between adjacent qubits, control biases, etc., which might negatively influence the quality of the annealing results. In this work, we aim at mitigating the effect of such biases for solving constrained optimization problems, by offering a two-step method, and apply it to Graph Partitioning. In the first step, we measure and reduce any biases that result from implementing the constraints of the problem. In the second, we add the objective function to the resulting bias-corrected implementation of the constraints, and send the problem to the quantum annealer. We apply this concept to Graph Partitioning, an important NP-hard problem, which asks to find a partition of the vertices of a graph that is balanced (the constraint) and minimizes the cut size (the objective). We first quantify the bias of the implementation of the constraint on the quantum annealer, that is, we require, in an unbiased implementation, that any two vertices have the same likelihood of being assigned to the same or to different parts of the partition. We then propose an iterative method to correct any such biases. We demonstrate that, after adding the objective, solving the resulting bias-corrected Ising problem on the quantum annealer results in a higher solution accuracy.","PeriodicalId":136482,"journal":{"name":"Proceedings of the 18th ACM International Conference on Computing Frontiers","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128367756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

DFShards

Proceedings of the 18th ACM International Conference on Computing Frontiers Pub Date : 1900-01-01 DOI: 10.1145/3457388.3458810

A. Yu., Yujuan Tan, Congcong Xu, Zhulin Ma, Duo Liu, Xianzhang Chen

{"title":"DFShards","authors":"A. Yu., Yujuan Tan, Congcong Xu, Zhulin Ma, Duo Liu, Xianzhang Chen","doi":"10.1145/3457388.3458810","DOIUrl":"https://doi.org/10.1145/3457388.3458810","url":null,"abstract":"The Miss Ratio Curve (MRC) describes the cache miss ratio as a function of the cache size. It has various shapes that represent the data access behaviors of workloads in the cache. MRC is an effective tool to guide cache partitioning, but its real-time construction is challenging. Miniature Simulation is a novel approach that constructs MRCs for non-stack algorithms in real time, via feeding a small number of sample references to multiple mini caches simultaneously to get the miss ratios. However, while using the Miniature Simulation, the size and number of mini-caches are difficult to set before the program runs. First, it may set too many mini-caches and cause repeated simulations. Second, it may miss some important cache sizes and consequently construct a less precise shape of MRC and result in incorrect cache partitioning. To address this problem, we propose DFShards, an adaptive cache shards (mini-caches) configuration approach based on program access patterns. The key idea is to dynamically adjust the configuration of the cache shards, including the number of the total cache shards and the size of each cache shard, based on the access behaviors to reflect changes in workload to build an precise MRC, thereby achieving better cache partitioning and overall performance. Our extensive experiments show that DFShards can construct precise MRCs in real-time during program running. Compared to the state-of-the-art approaches, it can save up to 47% of the cache space for MRC constructions while increasing the cache hit ratio by up to 17%.","PeriodicalId":136482,"journal":{"name":"Proceedings of the 18th ACM International Conference on Computing Frontiers","volume":"9 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120890517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

AvesTerra AvesTerra

Proceedings of the 18th ACM International Conference on Computing Frontiers Pub Date : 1900-01-01 DOI: 10.1145/3457388.3459986

J. Smart

{"title":"AvesTerra","authors":"J. Smart","doi":"10.1145/3457388.3459986","DOIUrl":"https://doi.org/10.1145/3457388.3459986","url":null,"abstract":"AvesTerra is a distributed knowledge representation framework for integrating many large and disparate data systems and analytic components at global scale. This framework allows data created or curated by many different institutions to be linked into a single unified, dynamic knowledge representation structure. The resulting fabric provides participants with a means to engage in multidisciplinary research and collaboration spanning many information systems without requiring a sophisticated computer science understanding of the mechanics of \"Big Data\" manipulation. Furthermore, AvesTerra enables this integration without the need for centralized data aggregation or local high-performance computational infrastructure, leveraging instead the distributed resources of a diverse and highly distributed analytic community. At a core technical level, AvesTerra consists of a system of peer-to-peer servers that collectively form a readily scalable knowledge space. The mathematical structure of this space is that of a generalized, recursive hypergraph, enabling the representation of complex dependency structures often encountered when working towards global scale. The framework incorporates numerous computational constructs including event publication and subscription, parallel threading and timer support, a unique distributed rendezvous mechanism for agent-based organization, privacy isolation, and semantic structure execution. This presentation provides an overview of the full framework and a sampling of the applications currently under development.","PeriodicalId":136482,"journal":{"name":"Proceedings of the 18th ACM International Conference on Computing Frontiers","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115449882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Diolkos

Proceedings of the 18th ACM International Conference on Computing Frontiers Pub Date : 1900-01-01 DOI: 10.1145/3457388.3458659

Oceane Bel, J. Pata, J. Vlimant, Nathan R. Tallent, J. Balcas, M. Spiropulu

引用次数: 1

Proceedings of the 18th ACM International Conference on Computing Frontiers 第18届ACM计算前沿国际会议论文集

Proceedings of the 18th ACM International Conference on Computing Frontiers Pub Date : 1900-01-01 DOI: 10.1145/3457388

引用次数: 0

ShuffleFL

Proceedings of the 18th ACM International Conference on Computing Frontiers Pub Date : 1900-01-01 DOI: 10.1145/3457388.3458665

Yuhui Zhang, Zhiwei Wang, Jiangfeng Cao, Rui Hou, Dan Meng

引用次数: 19

EVOLVE 进化

Proceedings of the 18th ACM International Conference on Computing Frontiers Pub Date : 1900-01-01 DOI: 10.1016/b978-0-323-44234-3.00002-6

A. Chazapis, Je Acquaviva, A. Bilas, G. Gardikis, C. Kozanitis, S. Louloudakis, Huy-Nam Nguyen, Christian Pinto, A. Scharl, Dimitrios Soudris

引用次数: 0