2022 IEEE High Performance Extreme Computing Conference (HPEC)最新文献_第7页

A High Throughput Hardware Accelerator for FFTW Codelets: A First Look FFTW代码的高吞吐量硬件加速器:初看

2022 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2022-09-19 DOI: 10.1109/HPEC55821.2022.9926333

L. Pileggi, Siyuan Chen, Keshav Harisrikanth, Guanglin Xu, K. Mai, F. Franchetti

引用次数: 0

GPU-Accelerated High-Bandwidth Radar Centroiding gpu加速高带宽雷达质心

2022 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2022-09-19 DOI: 10.1109/HPEC55821.2022.9926364

D. Brigada, Maximilian Merfeld, Kara Warner

引用次数: 0

Kv2vec: A Distributed Representation Method for Key-value Pairs from Metadata Attributes Kv2vec:元数据属性中键值对的分布式表示方法

2022 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2022-09-19 DOI: 10.1109/HPEC55821.2022.9926389

Chenxu Niu, Wei Zhang, S. Byna, Yong Chen

{"title":"Kv2vec: A Distributed Representation Method for Key-value Pairs from Metadata Attributes","authors":"Chenxu Niu, Wei Zhang, S. Byna, Yong Chen","doi":"10.1109/HPEC55821.2022.9926389","DOIUrl":"https://doi.org/10.1109/HPEC55821.2022.9926389","url":null,"abstract":"Distributed representation methods for words have been developed for years, and numerous methods exist, such as word2vec, GloVe, and fastText. However, they are not designed for key-value pairs, which is an important data pattern and widely used in many scenarios. For example, metadata attributes of scientific files consist of a collection of key-value pairs. In this research, we propose kv2vec, a method that captures relationships between keys and values and represents key-value pairs in dense vectors. The fundamental idea of the kv2vec method is utilizing recurrent neural networks (RNNs) with long short-term memory (LSTM) hidden units to convert each key-value pair to a distributed vector representation. This new method overcomes the weaknesses of existing embedding models for representing key-value pairs as vectors. Moreover, it can be integrated into dataset search solutions through querying metadata attributes for self-describing file formats that are widely used in HPC systems. We evaluate the kv2vec method with multiple real-world datasets, and the results show that kv2vec outperforms existing models.","PeriodicalId":200071,"journal":{"name":"2022 IEEE High Performance Extreme Computing Conference (HPEC)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132463817","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

Resource-Constrained Optimizations For Synthetic Aperture Radar On-Board Image Processing 合成孔径雷达机载图像处理的资源约束优化

2022 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2022-09-19 DOI: 10.1109/HPEC55821.2022.9926327

Maron Schlemon, M. Schulz, R. Scheiber

{"title":"Resource-Constrained Optimizations For Synthetic Aperture Radar On-Board Image Processing","authors":"Maron Schlemon, M. Schulz, R. Scheiber","doi":"10.1109/HPEC55821.2022.9926327","DOIUrl":"https://doi.org/10.1109/HPEC55821.2022.9926327","url":null,"abstract":"Synthetic Aperture Radar (SAR) can be used to create realistic and high-resolution 2D or 3D reconstructions of landscapes. The data capture is typically deployed using radar instruments in specially equipped, low flying planes, resulting in a large amount of raw data, which needs to be processed for image reconstruction. However, due to limited on-board processing capacities on the plane (power, size, weight, cooling, communication bandwidth to ground stations, etc.) and the need to capture many images during a single flight, the raw data must be processed on-board and then sent to the ground station efficiently as image products. In this paper we describe the processing architecture of the digital beamforming SAR (DBFSAR) of the German Areaospace Center (DLR) and the special steps that had to be taken to enable the on-board processing. We explain the required software optimizations and under which conditions their integration in the SAR imaging process leads to (near) real-time capability. We further describe the lessons learned in our work and discuss how they can be applied to other processing scenarios with limited resource availability.","PeriodicalId":200071,"journal":{"name":"2022 IEEE High Performance Extreme Computing Conference (HPEC)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132725135","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

Optimizing Designs Using Several Types of Memories on Modern FPGAs 在现代fpga上使用几种存储器的优化设计

2022 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2022-09-19 DOI: 10.1109/HPEC55821.2022.9926306

Mehmet Gungor, Kai Huang, Stratis Ioannidis, M. Leeser

引用次数: 0

AI and ML Accelerator Survey and Trends AI和ML加速器调查和趋势

2022 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2022-09-19 DOI: 10.1109/HPEC55821.2022.9926331

A. Reuther, P. Michaleas, Michael Jones, V. Gadepally, S. Samsi, J. Kepner

引用次数: 21

HuGraph: Acceleration of GCN Training on Heterogeneous FPGA Clusters with Quantization HuGraph:基于量化的异构FPGA集群GCN训练加速

2022 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2022-09-19 DOI: 10.1109/HPEC55821.2022.9926312

Letian Zhao, Qizhe Wu, Xiaotian Wang, Teng Tian, Wei Wu, Xi Jin

{"title":"HuGraph: Acceleration of GCN Training on Heterogeneous FPGA Clusters with Quantization","authors":"Letian Zhao, Qizhe Wu, Xiaotian Wang, Teng Tian, Wei Wu, Xi Jin","doi":"10.1109/HPEC55821.2022.9926312","DOIUrl":"https://doi.org/10.1109/HPEC55821.2022.9926312","url":null,"abstract":"Graph convolutional networks (GCNs) have suc-ceeded significantly in numerous fields, but the need for higher performance and energy efficiency training GCN on larger graphs continues unabated. At the same time, since recon-figurable accelerators have the ability to fine-grained custom computing modules and data movement, FPGAs can solve problems such as irregular memory access for GCN computing. Furthermore, to scale GCN computation, the use of heteroge-neous FPGAs is inevitable due to the constant iteration of new FPGAs. In this paper, we propose a novel framework, HuGraph, which automatically maps GCN training on heterogeneous FPGA clusters. With HuGraph, FPGAs work in synchronous data parallelism using a simple ring 1D topology that is suitable for most off-the-shelf FPGA clusters. HuGraph uses three approaches to advance performance and energy efficiency. First, HuGraph applies full-process quantization for neighbor-sampling-based data parallel training, thereby reducing computation and mem-ory consumption. Second, a novel balanced sampler is used to balance workloads among heterogeneous FPGAs so that FPGAs with fewer resources do not become bottlenecks in the cluster. Third, HuGraph schedules the execution order of GCN training to minimize time overhead. We implement a prototype on a single FPGA and evaluate cluster-level performance with a cycle-accurate simulator. Experiments show that HuGraph achieves up to 102.3 ×, 4.62×, and 11.1× speedup compared with the state-of-the-art works on CPU, GPU, and FPGA platforms, respectively, with negligible accuracy loss.","PeriodicalId":200071,"journal":{"name":"2022 IEEE High Performance Extreme Computing Conference (HPEC)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123602757","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: 1

Kalman Filter Driven Estimation of Community Structure in Time Varying Graphs 时变图中卡尔曼滤波驱动的社团结构估计

2022 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2022-09-19 DOI: 10.1109/HPEC55821.2022.9926358

L. Durbeck, P. Athanas

引用次数: 1

Fast Graph Algorithms for Superpixel Segmentation 超像素分割的快速图算法

2022 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2022-09-19 DOI: 10.1109/HPEC55821.2022.9926359

D. Floros, Tiancheng Liu, N. Pitsianis, Xiaobai Sun

引用次数: 1

Deep Gaussian process with multitask and transfer learning for performance optimization 基于多任务和迁移学习的深度高斯过程性能优化

2022 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2022-09-19 DOI: 10.1109/HPEC55821.2022.9926396

Wissam M. Sid-Lakhdar, M. Aznaveh, P. Luszczek, J. Dongarra

引用次数: 0