2016 IEEE High Performance Extreme Computing Conference (HPEC)最新文献_第6页

Distributed and configurable architecture for neuromorphic applications on heterogeneous cluster 异构集群上神经形态应用的分布式可配置架构

2016 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2016-09-01 DOI: 10.1109/HPEC.2016.7761598

Khadeer Ahmed, Qinru Qiu, M. Tamhankar

引用次数: 1

On SDN-based extreme-scale networks 在基于sdn的极端规模网络上

2016 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2016-09-01 DOI: 10.1109/HPEC.2016.7761621

Haitham Ghalwash, Chun-Hsi Huang

引用次数: 4

A hardware design for in-brain neural spike sorting 脑内神经脉冲分类的硬件设计

2016 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2016-09-01 DOI: 10.1109/HPEC.2016.7761590

Yinan Liu, Jiayi Sheng, M. Herbordt

引用次数: 8

Node level power measurements on a petaflop system 千万亿次浮点运算系统的节点级功率测量

2016 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2016-09-01 DOI: 10.1109/HPEC.2016.7761631

D. Brayford, C. Bernau, Carla Guillén, C. Navarrete

引用次数: 1

The BigDawg monitoring framework BigDawg监控框架

2016 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2016-09-01 DOI: 10.1109/HPEC.2016.7761642

Peinan Chen, V. Gadepally, M. Stonebraker

引用次数: 22

Polyhedral compilation for energy efficiency 能源效率的多面体编译

2016 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2016-09-01 DOI: 10.1109/HPEC.2016.7761595

B. Pradelle, M. Baskaran, Thomas Henretty, Benoît Meister, A. Konstantinidis, R. Lethin

引用次数: 0

Towards parallel implementation of associative inference for cogent confabulation 面向有说服力虚构的关联推理并行实现

2016 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2016-09-01 DOI: 10.1109/HPEC.2016.7761623

Zhe Li, Qinru Qiu, M. Tamhankar

{"title":"Towards parallel implementation of associative inference for cogent confabulation","authors":"Zhe Li, Qinru Qiu, M. Tamhankar","doi":"10.1109/HPEC.2016.7761623","DOIUrl":"https://doi.org/10.1109/HPEC.2016.7761623","url":null,"abstract":"The superb efficiency and noise resilience of human cognizance comes from the extensive highly associative memory. For example, it is easy for human to recognize occluded or incomplete text images based on its context. Associative inference in the neocortex system is a concurrent process. Serial implementation of this concurrent process not only hinders its performance, but also limits the quality of recall. This paper investigates parallel implementation of associative inference using cogent confabulation model, which is a highly cross-dependent and cyclic knowledge network that supports probabilistic inference. By breaking the fixed processing order, which is typical in sequential processing, and introducing randomness generated from the race conditions in parallel processing, we do not only reduce the runtime, but also improve the accuracy. Further improvement can be achieved by scheduling the lexicon processing intermittently, which provides time for the changes to settle down. Using sentence construction as a case study, we demonstrate that the parallel implementation provides up to 93.4% reduction in computation time and 5% improvement in recall accuracy.","PeriodicalId":308129,"journal":{"name":"2016 IEEE High Performance Extreme Computing Conference (HPEC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134559639","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

GPU accelerated, robust method for voxelization of solid objects 固体物体体素化的GPU加速鲁棒方法

2016 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2016-09-01 DOI: 10.1109/HPEC.2016.7761582

C. Nita, I. Stroia, L. Itu, C. Suciu, Viorel Mihalef, M. Datar, S. Rapaka, Puneet S. Sharma

{"title":"GPU accelerated, robust method for voxelization of solid objects","authors":"C. Nita, I. Stroia, L. Itu, C. Suciu, Viorel Mihalef, M. Datar, S. Rapaka, Puneet S. Sharma","doi":"10.1109/HPEC.2016.7761582","DOIUrl":"https://doi.org/10.1109/HPEC.2016.7761582","url":null,"abstract":"Solid voxelization represents the process of transforming a polygonal mesh into a voxel representation by associating each polygon of a mesh with the cells in the voxel grid. We introduce a novel approach for the voxelization of solid objects, designed for Graphics Processing Units (GPU). The method is based on a heuristic approach that computes an approximate distance field instead of using mesh surface normals or exact point-to-triangle distances. Two main steps are required: voxel marking and distance field computation. In the first step, each voxel is marked based on its location relative to the mesh (inside, outside of the domain or on its boundary), and, during the second step, a signed distance field is computed. Experiments focused on meshes encountered in medical imaging applications: a left ventricle and a coronary artery. The proposed method is found to be exceptionally robust as it is able to handle meshes with severe defects such as self intersections and holes. The GPU based implementation is on average 20 times faster than the multi-core CPU based implementation.","PeriodicalId":308129,"journal":{"name":"2016 IEEE High Performance Extreme Computing Conference (HPEC)","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132631024","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

PERFECT case studies demonstrating order of magnitude reduction in power consumption PERFECT案例研究证明了功耗的数量级降低

2016 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2016-09-01 DOI: 10.1109/HPEC.2016.7761612

David K. Wittenberg, E. Kadrić, A. DeHon, J. Edwards, Jeffrey E. Smith, S. Chiricescu

引用次数: 0

Novo-G#: Large-scale reconfigurable computing with direct and programmable interconnects novo - g#:具有直接和可编程互连的大规模可重构计算

2016 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2016-09-01 DOI: 10.1109/HPEC.2016.7761639

A. George, M. Herbordt, H. Lam, A. Lawande, Jiayi Sheng, Chen Yang

引用次数: 38