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Exascale computing, the next-generation computing environment, is expected to be applied to scientific and engineering applications. Accordingly, high-performance computing (HPC) technology is also being developed to improve the performance and high-speed parallelism of many-core processors. Previous researches on improving HPC performance have developed in the form of improving the overall system performance by analyzing the state of the system occurring in the range of the knowledge of expert. However, performance events occurring in a processor in a many-core environment have a large number of indicators, and it is difficult to analyze the correlation between them. In this paper, we propose an application-specific feature selection and clustering approach with HPC system profiling data. The proposed approach performs PCA-based feature selections for efficient performance analysis methods. In addition, the application-specific characteristics from profiling data can be analyzed by unsupervised learning. In our experiments, we evaluated highly parallel supercomputers with NAS parallel benchmark and were able to cluster applications efficiently.

This study aimed to develop a dynamic benchmark automation suite to measure a range of benchmark performances and evaluate the various high-performance computing (HPC) systems. Our suite supports an automated scaling test and profiling data based on hardware performance counters to analyze the system characteristics. We selected four HPC benchmarks—STREAM, High-Performance Linpack, High-Performance Conjugate Gradient, and NAS Parallel Benchmark-for experiments and configured testbeds based on five different systems and an Intel Knights Landing (KNL) cluster with 16 nodes. The Intel KNL system showed both unstable memory and high benchmark performances for a specific input range. Modern Intel systems also exhibited proper characteristics on compute-intensive workloads, whereas the up-to-date AMD system showed high efficiency and proper characteristics on memory-intensive and real application workloads. We also verified that each system has an optimal environment and characteristic for various combinations of experimental variables and profiling data.

Computational scientists and engineers who are eager to obtain the best performance of scientific applications need efficient application characterization methods to successfully exploit high-performance hardware resources. However, modern processors are accompanied by high-bandwidth on-chip memory or a large number of cores. Therefore, application characterization research that takes into account the newly introduced hardware features in next-generation high performance computing environments is insufficient and complex. In this paper, we propose a simple and fast method to classify the application characteristics in systems state-of-the-art processors using hardware performance counters. The proposed method utilizes hardware performance counters to monitor hardware events related to system performance. A clustering approach is adopted that requires limited understanding of the correlation between hardware events and application characteristics. The application characterization technique is applied to NAS parallel benchmarks in two systems, including Intel Knights Landing and SkyLake Xeon processors. We demonstrate that the proposed techniques can capture system and application characteristics and provide users with useful insights into application execution.

With the advent of Intels second-generation many-core processor (Knights Landing: KNL), high-bandwidth memory (HBM) with potentially five times more bandwidth than existing dynamic random-access memory has become available as a valuable computing resource for high-performance computing (HPC) applications. Therefore, resource management schemes should now be able to consider existing central processing unit cores, conventional main memory, and this newly available HBM to improve the overall system throughput and user response time. In this paper, we present our profiling mechanism and related scheduling policy that analyzes the resource usage patterns of various HPC workloads. By carefully allocating memory-intensive workloads to HBM in KNL, we show that the overall performance of multiple message passing interface workloads can be improved in terms of the execution time and system utilization. We evaluate and verify the effectiveness of our scheme for optimizing the use of HBM by using NAS Parallel Benchmarks.

Herein, we evaluated and analyzed the major benchmark performance of various multiple-core systems to identify their structures and characteristics. To this end, we developed a benchmark automation tool and selected five Intel and AMD systems. We also chose three key benchmarks, namely STREAM, High-Performance Linpack (HPL), and High-Performance Conjugate Gradient (HPCG), to evaluate the memory, CPU, and aggregate performance. In the STREAM experiment, the high-bandwidth memory (MCDRAM) performance of the KNL was reduced by more than 50% at a specific point owing to the accuracy of the input array size which can be ignored in DDR memory. In the HPL experiment, KNL using MCDRAM exhibited the best optimization performance, but in the experiment without optimization, the performance of MCDRAM was rather lower than DDR or cache. Thus, MCDRAM code optimization may be required to utilize MCDRAM at peak performance in the many-core environment. In the HPCG experiment, the performance variance was large depending on the combination of the MPI process and the number of shared threads. When the number of MPI processes is set to 2 or 4 and the total number of shared threads is equal to the number of physical cores in the system, excellent performance was obtained. Moreover, the maximum performance of each single system was proportional to the memory performance.

Hardware performance counters in processors are mainly used for low level performance analysis and application tuning by monitoring performance-related hardware events. With the advent of processors with more cores than existing multicore processors and additional high-bandwidth memory, research on the performance analysis of new systems has received increasing attention from the high-performance computing community. Analyzing application characteristics and system features in a new system is essential for computational scientists and engineers who are eager to obtain the best performance of their scientific applications. However, these processors, increased core counts and high-performance resources, make it difficult to understand the correlation between performance-related hardware events. In this paper, we propose a method to simply and quickly classify application characteristics by using a data mining tool without understanding the correlation between hardware events. When we applied the proposed method to NAS Parallel Benchmarks (NPB), the application characteristics were the same as the authorized NPB categories. We show the effectiveness of the proposed scheme in a case study on analyzing the degree of interference between application characteristics.

AMGA (ARDA Metadata Grid Application) is a grid metadata catalog system, that is part of the gLite middleware, developed for the EGEE (Enabling Grids for E-sciencE) project. Since the end of EGEE, gLite has participated in the European Middleware Initiative (EMI) with other major middleware systems such as ARC, dCache and UNICORE. Through AMGA’s three-year EMI collaboration, it has been actively developed, tested and deployed. Previously, AMGA had made progress by focusing on development for the purposes of function implementation and improvement as a metadata catalogue system. After participating in the EMI, it has designed and implemented middleware-interoperability functions according to EMI policies and strategies.

This paper presents design and strategy of KSSI (Korea Supercomputing Software Initiative) which aims to facilitate interoperability among different middleware technologies over national supercomputing infrastructures, provide guidelines for deployment and quality assurance of developed software, and present a grand-challenge problem that requires massive computing/storage power and seamless integration of various supercomputing technologies. Throughout our KSSI strategy and system, we develop and maintain a set of high-quality software technologies that can support most challenging scientific applications.

AMGA (ARDA Metadata Grid Application) is one of main EMI (European Middleware Initiative) middleware software for the EMI project which started from 2010 and lasted 3 years. In this time, AMGA metadata catalog technology had the good opportunity to enlarge the range of relation with other EMI middleware components. Especially, AMGA interoperability with several EMI components paved the way for providing usefulness of management and searching available AMGA resource to AMGA users. Additionally, AMGA GUIs (AMGA Manager and AMGA Web application) have been developed focusing on user’s utilization. In this article, we present not only AMGA development experience of in the EMI project but also other activities related to expand use of AMGA.

Falls are the primary cause of accidents in people over the age of 65, and frequently lead to serious injuries. Since the early detection of falls is an important step to alert and protect the aging population, a variety of research on detecting falls was carried out including the use of accelerators, gyroscopes and tilt sensors. In exiting studies, falls were detected using an accelerometer with errors. In this study, the proposed method for detecting falls was to use two accelerometers to reject wrong falls detection. As falls are accompanied by the acceleration of gravity and rotational motion, the falls in this study were detected by using the z-axial acceleration differences between two sites. The falls were detected by calculating the difference between the analyses of accelerometers placed on two different positions on the chest of the subject. The parameters of the maximum difference of accelerations (diff_Z) and the integration of accelerations in a defined region (Sum_diff_Z) were used to form the fall detection algorithm. The falls and the activities of daily living (ADL) could be distinguished by using the proposed parameters without errors in spite of the impact and the change in the positions of the accelerometers. By comparing each of the axial accelerations, the directions of falls and the condition of the subject afterwards could be determined.In this study, by using two accelerometers without errors attached to two sites to detect falls, the usefulness of the proposed fall detection algorithm parameters, diff_Z and Sum_diff_Z, were confirmed., {"references":["Bijan Najafi, Kamiar Aminian, Anisiara Paraschiv-Ionescu, Francois\nLoew, Chrisophe J. Dula and Philippe Robert, \"Ambulatory system for\nhuman motion analysis using a kinematic sensor: monitoring of daily\nphysical activity in the elderly\", IEEE transactions on biomedical\nengineering, Vol 50, No. 6, pp. 711-723, 2003.","M. J. Mathie, A. C. F. Coster, N. H. Lovell and B. G. Celler, \"A pilot\nstudy of long term monitoring of human movement in the home using\naccelerometry\", J. Telemed. Telecare, Vol. 10, pp. 144-151, 2004","B. G. Steele, L. Holt, Belza, S. M. Ferris, S. Lakshminaryan and D. M.\nBuchner, \"Quantitating physical activity in COPD using a triaxial\naccelerometer\", Chest, Vol. 117, pp. 1359-1367, 2000.","K. V. Laerhoven and O. Cakmakci, \"What shall we teach our pants?\",\nThe 4th international Symposium on wearable computers (ISWC2000),\npp. 77-83, 2000.","Bijan Najafi, Kamiar Aminian, Anosoara Parachiv-Ionescu, Francois\nLoew, Christophe J. Bula and Philippe Robert, \"Ambulatory system for\nhuman motion analysis using a kinematic sensor: monitoring of daily\nphysical activity in the elderly\", IEEE transaction on biomedical\nengineering, Vol. 50, No. 6, pp. 711-722, 2003.","T. Ryan Burchfield and S. Venkatesan, \"Accelerometer-based human\nabnormal movement detection in wireless sensor networks\",\nInternational conference in mobile systems, Applications and services,\npp. 67-69, 2005.","Dean M. Karantonis, Michael R. Narayanan, M. Mathie, Nigel H. Lovell\nand G. Celler, \"Implementation of a real-time human movement\nclassifier using a traixial accellerometer for ambulatory monitoring\",\nIEEE transaction on information technology in biomedicine, Vol. 10,\nNo. 1, 2006. [11] Maarit Kangas, Antti Konttila, Ilkka Winblad and\nTimo J├ñms├ñ, \"Determination of simple thresholds for accelerometrybased\nparameters for falls detection\", Proceedings of the 29th Annual\nInternational Conference of the IEEE EMBS Cité Internationale, Lyon,\nFrance pp. 23-26, 8, 2007.","Garrett Brown, \"An accelerometer based falls detector : development,\nExperimentation, and Analysis\", University of California. Berkeley,\n2005.","Yie-Tarng Chen, Yu-Ching Lin, and Wen-Hsien Fang, \"A Video-based\nhuman falls detection system for smart homes\", Journal of the Chinese\nInstitute of Engineers, Vol. 33, No. 5, pp. 681-690,2010.\n[10] A. Elmoataz , \"falls detection Using body volume recontruction and\nvertical repartition analysis\", ICISP 2010, LNCS 6134, pp. 376-383,\n2010."]}

This paper describes the recent improvement of the AMGA (ARDA Metadata Grid Application) python client library for the Belle II Experiment. We were drawn to the action items related to library improvement after in-depth discussions with the developer of the Belle II distributed computing system. The improvement includes client-side metadata federation support in python, DIRAC SSL library support as well as API refinement for synchronous operation. Some of the improvements have already been applied to the AMGA python client library as bundled with the Belle II distributed computing software. The recent mass Monte- Carlo (MC) production campaign shows that the AMGA python client library is reliably stable.

AMGA (ARDA Metadata Grid Application) is a grid metadata catalogue system that has been developed as a component of the EU FP7 EMI consortium based on the requirements of the HEP (High-Energy Physics) and the biomedical user communities. Currently, AMGA is exploited to manage the metadata in the gBasf2 framework at the Belle II experiment, one of the largest particle physics experiments in the world. In this paper, we present our efforts to optimize the metadata query performance of AMGA to better support the massive MC Campaign of the Belle II experiment. Although AMGA exhibits very outstanding performance for a relatively small amount of data, as the number of directories and the metadata size increase (e.g. hundreds of thousands of directories) during the MC Campaign, AMGA suffers from severe query processing performance degradation. To address this problem, we modified the query search mechanism and the database scheme of AMGA to provide dramatic improvements of metadata search performance and query response time. Throughout our comparative performance analysis of metadata search operations, we show that AMGA can be an optimal solution for a metadata catalogue in a large-scale scientific experimental framework