2019 International Conference on High Performance Computing & Simulation (HPCS)最新文献_第8页

Fixed-point self-tuning CPU performance controller for Linux kernel Linux内核的定点自调优CPU性能控制器

2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188078

Michal Getka, M. Karpowicz

引用次数: 0

Field Programmable Gate Array Technology as an Enabling Tool Towards Large-Neighborhood Cellular Automata on Cells with Many States 现场可编程门阵列技术在多状态元胞上实现大邻域元胞自动机的工具

2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188084

Nikolaos Kyparissas, A. Dollas

{"title":"Field Programmable Gate Array Technology as an Enabling Tool Towards Large-Neighborhood Cellular Automata on Cells with Many States","authors":"Nikolaos Kyparissas, A. Dollas","doi":"10.1109/HPCS48598.2019.9188084","DOIUrl":"https://doi.org/10.1109/HPCS48598.2019.9188084","url":null,"abstract":"Cellular Automata (CA) have been used for many decades to simulate physical processes. From the $3 times 3$ and $5 times 5$ neighborhoods of the 1950’s, and typically on binary images, as recently as the mid-2010’s the neighborhoods went up to $15 times 15$ on images with a few states. Field Programmable Gate Array (FPGA) technology, already applicable to CA simulation since the early 1990’s, has reached such maturity levels that a small device can simulate large-neighborhood CA. In this work we present an architecture which we have fully implemented, that can simulate CA with up to $29 times 29$ neighborhoods on 256-state cells for Full High Definition (FHD) image input/output with real-time 60 frames-per-second capability. Emphasis of the present work is on the game-changing opportunities that FPGA technology creates to the CA community. We present results from the Greenberg-Hastings and Hodgepodge models, as well as a large-neighborhood anisotropic model. Large neighborhoods either yield qualitatively different results vs. smaller neighborhoods, or lead to results which are merely impossible to produce with small neighborhoods. A comparison of FPGA technology for CA shows advantages vs. conventional Central Processing Units (CPUs) or Graphics Processor Units (GPUs).","PeriodicalId":371856,"journal":{"name":"2019 International Conference on High Performance Computing & Simulation (HPCS)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123725014","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

Detecting Selected Network Covert Channels Using Machine Learning 使用机器学习检测选定的网络隐蔽通道

2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188115

Mehdi Chourib

引用次数: 10

Parallel algorithm for prediction of variables in Simultaneous Equation Models 联立方程模型中变量预测的并行算法

2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188089

Óscar Gómez, J. López-Espín, A. P. Benavent

引用次数: 0

CRESCO ENEA HPC clusters: a working example of a multifabric GPFS Spectrum Scale layout CRESCO ENEA HPC集群:一个多结构GPFS频谱规模布局的工作示例

2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188135

F. Iannone, F. Ambrosino, G. Bracco, M. D. Rosa, A. Funel, G. Guarnieri, S. Migliori, Filippo Palombi, Giovanni Ponti, G. Santomauro, P. Procacci

{"title":"CRESCO ENEA HPC clusters: a working example of a multifabric GPFS Spectrum Scale layout","authors":"F. Iannone, F. Ambrosino, G. Bracco, M. D. Rosa, A. Funel, G. Guarnieri, S. Migliori, Filippo Palombi, Giovanni Ponti, G. Santomauro, P. Procacci","doi":"10.1109/HPCS48598.2019.9188135","DOIUrl":"https://doi.org/10.1109/HPCS48598.2019.9188135","url":null,"abstract":"EXTENDED ABSTRACT ENEA is the Italian National Agency for New Technologies, Energy and Sustainable Economic Development. ENEA operates in many sectors among which the most important are: energy technologies, materials physics, life sciences and climate. In the framework of its institutional mission, the ICT Division provides computing and storage resources integrated into ENEAGRID/CRESCO, an infrastructure distributed over 6 sites, whose main facilities are the HPC CRESCO clusters. The bulk of all storage is based on IBM Spectrum Scale (GPFS) since many years. The access to data, even over WAN, is managed by GPFS clusters. In May 2018 the new cluster CRSCO6 was inaugurated. CRSCO6, a 1.4 Pflops based on Intel Xeon X86-64 SkyLake CPU ranked at 420th of TOP 500 Nov.2018 list. While the interconnection of CRESCO6 is based on Intel Omni-Path (OPA) (100 Gbps), the previous CRESCO4 and CRESCO5 clusters have a network based on InfniBand QDR Truescale fabric (40 Gbps). Hence, in order to provides storage to all CRESCO clusters a GPFS multifabric layout has been implemented after dedicated tests. The work describes the ENEAGRID/CRESCO infrastructure and in particular the solution adopted to implement the GPFS multifabric.","PeriodicalId":371856,"journal":{"name":"2019 International Conference on High Performance Computing & Simulation (HPCS)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125515455","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: 68

Fast and robust PRNGs based on jumps in N-cubes for simulation, but not exclusively for that. 基于n个立方体跳跃的快速稳健prng模拟，但并不仅限于此。

2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188175

S. Contassot-Vivier, Jean-François Couchot, Mohammed Bakiri, Pierre-Cyrille Héam

引用次数: 0

Novel algorithm to extract multiple solutions for RNA sequence classification problem RNA序列分类问题的多解提取新算法

2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188203

Naoual Guannoni, F. Mhamdi, Emanuel Weitschek, M. Elloumi

{"title":"Novel algorithm to extract multiple solutions for RNA sequence classification problem","authors":"Naoual Guannoni, F. Mhamdi, Emanuel Weitschek, M. Elloumi","doi":"10.1109/HPCS48598.2019.9188203","DOIUrl":"https://doi.org/10.1109/HPCS48598.2019.9188203","url":null,"abstract":"Knowledge extraction methods from Next Generation Sequencing Data (NGS) are highly requested nowadays. This technology has led to an explosion in the amount of genomic data. However, the efficiency of N GS has posed a challenge for analysis this vast genomic data, gene interaction and expression studies. In this work, we focus on RNA-seq gene expression analysis and specifically of cancer disease studies with rule-based supervised classification algorithms that build a model able to discriminate tumoral to normal cases. State of the art algorithms compute just a single classification model that contains few features. On the contrary, the goal is to elicit a higher amount of knowledge by computing many classification models, and therefore to identify most of the features related to an investigated class. Major efforts have been made in this field with rule-based algorithms (CAMUR method) and an initial step has been realized with tree-based ones. In this paper, we propose a new method that extracts multiple and equivalent classification methods. This method integrates a rule-based classification method and a feature elimination technique in order to obtain more compact, exact, and interpretable models in a reduced execution time. We analyze an RNA-seq of breast cancer data set extracted from The Cancer Genome Atlas (TCGA) and we compare our results with the existing method (CAMUR). Experimental results show the efficacy of our proposed method. We obtain several reliable and efficient classification models compared to CAMUR method. Also, our method is faster than CAMUR algorithm.","PeriodicalId":371856,"journal":{"name":"2019 International Conference on High Performance Computing & Simulation (HPCS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128365771","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

Surrogate Modelling for Efficient Discovery of Emergent Population Dynamics 有效发现新兴种群动态的代理模型

2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188208

James Pyle, M. Chimeh, P. Richmond

{"title":"Surrogate Modelling for Efficient Discovery of Emergent Population Dynamics","authors":"James Pyle, M. Chimeh, P. Richmond","doi":"10.1109/HPCS48598.2019.9188208","DOIUrl":"https://doi.org/10.1109/HPCS48598.2019.9188208","url":null,"abstract":"Outcomes of simulating complex systems models, such as emergent properties and desirable system level behaviours, can be discovered via heuristic techniques such as Genetic Algorithms (GAs). Using simulation as the cost function evaluation for a GA (i.e. simulation guided search) is computationally expensive. Additionally the GA search process may require many generations before high quality solutions can be discovered. As such, simulation guided search can be considered high latency with respect to discovery of a range of high quality solutions. In this paper we experimentally demonstrate that the time to discovery of high quality solutions can be reduced through a low latency, hybrid GA search using a machine learning surrogate model trained to approximate simulation via large amounts of batched parallel simulation data generated in a HPC environment. Using a common population dynamics model optimised for GPU simulation by the FLAME GPU framework, we directly compare the hybrid approach with simulation guided search to understand the relationship between computational cost and quality of prediction. Our results indicate that given equivalent levels of simulation investment, results of equivalent quality can be obtained. The hybrid approach is however able to reduce the latency of the GA search process by shifting the computational cost of simulation to a highly parallel pre-search step used to train surrogate models.","PeriodicalId":371856,"journal":{"name":"2019 International Conference on High Performance Computing & Simulation (HPCS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128241488","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

Why applications are coupled to I/O and how the scenery can be changed 为什么应用程序要耦合到I/O，以及如何改变这种情况

2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188205

F. Nikolaidis, S. Zertal, Thomas Leibovici, Athanasios Kiatipis

{"title":"Why applications are coupled to I/O and how the scenery can be changed","authors":"F. Nikolaidis, S. Zertal, Thomas Leibovici, Athanasios Kiatipis","doi":"10.1109/HPCS48598.2019.9188205","DOIUrl":"https://doi.org/10.1109/HPCS48598.2019.9188205","url":null,"abstract":"The versatility and I/O demanding nature of modern applications, compel them to be operable across various storage platforms. As the platforms may exhibit different characteristics, the applications need to incorporate platform-specific code paths. More demanding applications that require concurrent use of platforms must also implement transactions, parallel streams, error handling routines and data reconstruction strategies. Building this logic is a complicated task that requires I/O expertise, but more importantly, it must be re-implemented for each application. To that end, the application decouples from the platform but is bounded to I/O related code paths. This paper presents a framework (TrIO) to decouple the I/O logic from the application source code. The framework exhibits a domain specific language, that allows the developers to model the I/O logic as a graph of components, without any implementation concerns. The runtime engine takes as input the graph and generates the respective intransit data processing network with transactional and parallel data delivery guarantees. As a proof-of-concept, a distributed filesystem prototype built atop the engine, with graphs that imitate RedHat GlusterFS volumes to define its behavior. Conducted tests show that for volumes that rely on parallelization, our prototype can outperform GlusterFS by a factor of 2.","PeriodicalId":371856,"journal":{"name":"2019 International Conference on High Performance Computing & Simulation (HPCS)","volume":"31 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130391138","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

High Performance Multilevel Graph Partitioning on GPU GPU上的高性能多级图分区

2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188120

B. Goodarzi, Farzad Khorasani, Vivek Sarkar, D. Goswami

{"title":"High Performance Multilevel Graph Partitioning on GPU","authors":"B. Goodarzi, Farzad Khorasani, Vivek Sarkar, D. Goswami","doi":"10.1109/HPCS48598.2019.9188120","DOIUrl":"https://doi.org/10.1109/HPCS48598.2019.9188120","url":null,"abstract":"Graph partitioning is a common computational phase in many application domains, including social network analysis, data mining, scheduling, and VLSI design. The significant SIMT compute power of a GPU makes it an appropriate platform to exploit data parallelism in graph partitioning and accelerate the computation. However, irregular, non-uniform, and data-dependent graph partitioning sub-tasks pose multiple challenges for efficient GPU utilization. Some of these challenges include load imbalance, non-coalesced memory accesses, and warp execution inefficiency. In this paper, we describe an effective and methodological approach to enable multi-level graph partitioning on GPUs. Our solution avoids thread divergence and balances the load over GPU threads by dynamically assigning appropriate number of threads to process the graph vertices and their irregular sized neighbors. Our design is autonomous, i.e., all the steps are carried out by the GPU with minimal CPU involvement, which is required for a range of GPU applications as a pre-processing step. We show that our approach performs better and is comparable in partitioning quality with respect to the state-of-the-art CPU-based parallel graph partitioner (mtmetis). Moreover, to the best of our knowledge, it is the first autonomous approach on GPU.","PeriodicalId":371856,"journal":{"name":"2019 International Conference on High Performance Computing & Simulation (HPCS)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134404937","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