R. Salvador, S. Ortega, D. Madroñal, H. Fabelo, R. Lazcano, G. Callicó, E. Juárez, R. Sarmiento, C. Sanz
The HELICoiD project is a European FP7 FET Open funded project. It is an interdisciplinary work at the edge of the biomedical domain, bringing together neurosurgeons, computer scientists and electronic engineers. The main target of the project was to provide a working demonstrator of an intraoperative image-guided surgery system for real-time brain cancer detection, in order to assist neurosurgeons during tumour resection procedures. One of the main problems associated to brain tumours is its infiltrative nature, which makes complete tumour resection a highly difficult task. With the combination of Hyperspectral Imaging and Machine Learning techniques, the project aimed at demonstrating that a precise determination of tumour boundaries was possible, helping this way neurosurgeons to minimize the amount of removed healthy tissue. The project partners involved, besides different universities and companies, two hospitals where the demonstrator was tested during surgical procedures. This paper introduces the difficulties around brain tumor resection, stating the main objectives of the project and presenting the materials, methodologies and platforms used to propose a solution. A brief summary of the main results obtained is also included.
HELICoiD项目是欧洲FP7 FET Open资助的项目。它是生物医学领域边缘的一项跨学科工作，汇集了神经外科医生、计算机科学家和电子工程师。该项目的主要目标是提供术中图像引导手术系统的工作演示，用于实时脑癌检测，以便在肿瘤切除过程中协助神经外科医生。与脑肿瘤相关的主要问题之一是其浸润性，这使得完全切除肿瘤是一项非常困难的任务。通过结合高光谱成像和机器学习技术，该项目旨在证明精确确定肿瘤边界是可能的，从而帮助神经外科医生最大限度地减少切除健康组织的数量。除了不同的大学和公司外，项目合作伙伴还涉及两家医院，在那里演示器在手术过程中进行了测试。本文介绍了脑肿瘤切除的困难，说明了该项目的主要目标，并介绍了提出解决方案所使用的材料、方法和平台。本文还简要总结了所获得的主要结果。
{"title":"HELICoiD: interdisciplinary and collaborative project for real-time brain cancer detection: Invited Paper","authors":"R. Salvador, S. Ortega, D. Madroñal, H. Fabelo, R. Lazcano, G. Callicó, E. Juárez, R. Sarmiento, C. Sanz","doi":"10.1145/3075564.3076262","DOIUrl":"https://doi.org/10.1145/3075564.3076262","url":null,"abstract":"The HELICoiD project is a European FP7 FET Open funded project. It is an interdisciplinary work at the edge of the biomedical domain, bringing together neurosurgeons, computer scientists and electronic engineers. The main target of the project was to provide a working demonstrator of an intraoperative image-guided surgery system for real-time brain cancer detection, in order to assist neurosurgeons during tumour resection procedures. One of the main problems associated to brain tumours is its infiltrative nature, which makes complete tumour resection a highly difficult task. With the combination of Hyperspectral Imaging and Machine Learning techniques, the project aimed at demonstrating that a precise determination of tumour boundaries was possible, helping this way neurosurgeons to minimize the amount of removed healthy tissue. The project partners involved, besides different universities and companies, two hospitals where the demonstrator was tested during surgical procedures. This paper introduces the difficulties around brain tumor resection, stating the main objectives of the project and presenting the materials, methodologies and platforms used to propose a solution. A brief summary of the main results obtained is also included.","PeriodicalId":398898,"journal":{"name":"Proceedings of the Computing Frontiers Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125530182","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}
D. Palossi, Andres Gomez, Stefan Draskovic, K. Keller, L. Benini, L. Thiele
Nowadays nano Unmanned Aerial Vehicles (UAV's), such as quad-copters, have very limited flight times, tens of minutes at most. The main constraints are energy density of the batteries and the engine power required for flight. In this work, we present a nano-sized blimp platform, consisting of a helium balloon and a rotorcraft. Thanks to the lift provided by helium, the blimp requires relatively little energy to remain at a stable altitude. We also introduce the concept of duty-cycling high power actuators, to reduce the energy requirements for hovering even further. With the addition of a solar panel, it is even feasible to sustain tens or hundreds of flight hours in modest lighting conditions (including indoor usage). A functioning 52 gram prototype was thoroughly characterized and its lifetime was measured in different harvesting conditions. Both our system model and the experimental results indicate our proposed platform requires less than 200 mW to hover in a self sustainable fashion. This represents, to the best of our knowledge, the first nano-size UAV for long term hovering with low power requirements.
{"title":"Self-Sustainability in Nano Unmanned Aerial Vehicles: A Blimp Case Study","authors":"D. Palossi, Andres Gomez, Stefan Draskovic, K. Keller, L. Benini, L. Thiele","doi":"10.1145/3075564.3075580","DOIUrl":"https://doi.org/10.1145/3075564.3075580","url":null,"abstract":"Nowadays nano Unmanned Aerial Vehicles (UAV's), such as quad-copters, have very limited flight times, tens of minutes at most. The main constraints are energy density of the batteries and the engine power required for flight. In this work, we present a nano-sized blimp platform, consisting of a helium balloon and a rotorcraft. Thanks to the lift provided by helium, the blimp requires relatively little energy to remain at a stable altitude. We also introduce the concept of duty-cycling high power actuators, to reduce the energy requirements for hovering even further. With the addition of a solar panel, it is even feasible to sustain tens or hundreds of flight hours in modest lighting conditions (including indoor usage). A functioning 52 gram prototype was thoroughly characterized and its lifetime was measured in different harvesting conditions. Both our system model and the experimental results indicate our proposed platform requires less than 200 mW to hover in a self sustainable fashion. This represents, to the best of our knowledge, the first nano-size UAV for long term hovering with low power requirements.","PeriodicalId":398898,"journal":{"name":"Proceedings of the Computing Frontiers Conference","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129547867","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}
Clouds hide the complexity of maintaining a physical infrastructure with a disadvantage: they also hide their internal workings. Should users need to know about these details e.g., to increase the reliability or performance of their applications, they would need to detect slight behavioural changes in the underlying system. Existing solutions for such purposes offer limited capabilities. This paper proposes a technique for predicting background workload by means of simulations that are providing knowledge of the underlying clouds to support activities like cloud orchestration or workflow enactment. We propose these predictions to select more suitable execution environments for scientific workflows. We validate the proposed prediction approach with a biochemical application.
{"title":"Cloud Workload Prediction by Means of Simulations","authors":"G. Kecskeméti, A. Kertész, Z. Németh","doi":"10.1145/3075564.3075589","DOIUrl":"https://doi.org/10.1145/3075564.3075589","url":null,"abstract":"Clouds hide the complexity of maintaining a physical infrastructure with a disadvantage: they also hide their internal workings. Should users need to know about these details e.g., to increase the reliability or performance of their applications, they would need to detect slight behavioural changes in the underlying system. Existing solutions for such purposes offer limited capabilities. This paper proposes a technique for predicting background workload by means of simulations that are providing knowledge of the underlying clouds to support activities like cloud orchestration or workflow enactment. We propose these predictions to select more suitable execution environments for scientific workflows. We validate the proposed prediction approach with a biochemical application.","PeriodicalId":398898,"journal":{"name":"Proceedings of the Computing Frontiers Conference","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115238310","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}
The "memory wall" problem requires not only the use of increasingly aggressive techniques designed to reduce the latency of memory system, but also the raise of more accurate memory metrics. C-AMAT, an extension of AMAT that considers both locality and concurrency of memory accesses, can evaluate the performance of modern memory system more accurately. However, C-AMAT only involves those cycles consumed by memory accesses, ignoring the blocked time caused by some techniques like hardware prefetch, which may result in inaccurate evaluation. In this paper, we propose a more comprehensive memory metric called Blocked C-AMAT (BC-AMAT). It extends C-AMAT to take the blocked cycles into consideration. Experimental results show that BC-AMAT correlates much better with IPC than C-AMAT does when a few prefetch strategies are applied both in single-core mode and multi-core mode. In addition, a case study is provided in which BC-AMAT is used to adjust prefetching degree dynamically. The result shows that BC-AMAT achieves higher performance improvement than C-AMAT, demonstrating its usefulness in system optimization. BC-AMAT is more accurate and comprehensive than C-AMAT in evaluating modern memory systems, meanwhile, provides more insight for architecture design.
{"title":"BC-AMAT: Considering Blocked Time in Memory System Measurement","authors":"Qi Yu, Libo Huang, Cheng Qian, Zhiying Wang","doi":"10.1145/3075564.3076264","DOIUrl":"https://doi.org/10.1145/3075564.3076264","url":null,"abstract":"The \"memory wall\" problem requires not only the use of increasingly aggressive techniques designed to reduce the latency of memory system, but also the raise of more accurate memory metrics. C-AMAT, an extension of AMAT that considers both locality and concurrency of memory accesses, can evaluate the performance of modern memory system more accurately. However, C-AMAT only involves those cycles consumed by memory accesses, ignoring the blocked time caused by some techniques like hardware prefetch, which may result in inaccurate evaluation. In this paper, we propose a more comprehensive memory metric called Blocked C-AMAT (BC-AMAT). It extends C-AMAT to take the blocked cycles into consideration. Experimental results show that BC-AMAT correlates much better with IPC than C-AMAT does when a few prefetch strategies are applied both in single-core mode and multi-core mode. In addition, a case study is provided in which BC-AMAT is used to adjust prefetching degree dynamically. The result shows that BC-AMAT achieves higher performance improvement than C-AMAT, demonstrating its usefulness in system optimization. BC-AMAT is more accurate and comprehensive than C-AMAT in evaluating modern memory systems, meanwhile, provides more insight for architecture design.","PeriodicalId":398898,"journal":{"name":"Proceedings of the Computing Frontiers Conference","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124723343","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}
Comfort zone is an artificial mental boundary within which you maintain a sense of security. A couple of years ago, PRACE (Partnership for Advanced Computing in Europe) challenged the technology providers of Europe in proposing new architectures, new concepts and building a High-Performance Computer system mixing old and proven technology with advanced new components. E4 took the challenge and proposed an innovative and uncomfortable approach: DAVIDE. The talk will present the rationale for the technological and architectural choices done for building DAVIDE, the key innovative concepts, the software ecosystems and some preliminary performance.
{"title":"Extending the comfort zone: DAVIDE","authors":"F. Magugliani","doi":"10.1145/3075564.3095084","DOIUrl":"https://doi.org/10.1145/3075564.3095084","url":null,"abstract":"Comfort zone is an artificial mental boundary within which you maintain a sense of security. A couple of years ago, PRACE (Partnership for Advanced Computing in Europe) challenged the technology providers of Europe in proposing new architectures, new concepts and building a High-Performance Computer system mixing old and proven technology with advanced new components. E4 took the challenge and proposed an innovative and uncomfortable approach: DAVIDE. The talk will present the rationale for the technological and architectural choices done for building DAVIDE, the key innovative concepts, the software ecosystems and some preliminary performance.","PeriodicalId":398898,"journal":{"name":"Proceedings of the Computing Frontiers Conference","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129169729","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}
This paper uses betweenness centrality as a case study to research efficient work stealing in a heterogeneous system environment. Betweenness centrality is an important algorithm in graph processing. It presents multiple-level parallelism and is an interesting problem to exploit various optimizations. We investigate queue-based work stealing to distribute its tasks across GPU compute units (CUs) and across the CPU and the GPU, which has not been done by prior work. In particular, we demonstrate how to leverage the new platform-atomic operations on AMD Accelerated Processing Units (APUs) to operate cross-device queues in a lock-free manner in shared virtual memory. To make the work stealing runtime and the application more efficient, we apply new architectural features, including atomic operations with different memory scopes and or-derings for different synchronization scenarios. We implement our solution using heterogeneous system architecture (HSA). Our results show that betweenness centrality with CPU-GPU work stealing achieves an average of 15% (up to 30%) performance improvement over GPU-only execution for diverse graph inputs. Our work stealing solution can be applied widely to other applications too. Finally, we analyze important parameters critical for queuing and stealing.
{"title":"Work Stealing in a Shared Virtual-Memory Heterogeneous Environment: A Case Study with Betweenness Centrality","authors":"Shuai Che, Marc S. Orr, J. Gallmeier","doi":"10.1145/3075564.3075567","DOIUrl":"https://doi.org/10.1145/3075564.3075567","url":null,"abstract":"This paper uses betweenness centrality as a case study to research efficient work stealing in a heterogeneous system environment. Betweenness centrality is an important algorithm in graph processing. It presents multiple-level parallelism and is an interesting problem to exploit various optimizations. We investigate queue-based work stealing to distribute its tasks across GPU compute units (CUs) and across the CPU and the GPU, which has not been done by prior work. In particular, we demonstrate how to leverage the new platform-atomic operations on AMD Accelerated Processing Units (APUs) to operate cross-device queues in a lock-free manner in shared virtual memory. To make the work stealing runtime and the application more efficient, we apply new architectural features, including atomic operations with different memory scopes and or-derings for different synchronization scenarios. We implement our solution using heterogeneous system architecture (HSA). Our results show that betweenness centrality with CPU-GPU work stealing achieves an average of 15% (up to 30%) performance improvement over GPU-only execution for diverse graph inputs. Our work stealing solution can be applied widely to other applications too. Finally, we analyze important parameters critical for queuing and stealing.","PeriodicalId":398898,"journal":{"name":"Proceedings of the Computing Frontiers Conference","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131968975","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}
S. Fiore, Cosimo Palazzo, Alessandro D'Anca, D. Elia, E. Londero, C. Knapic, S. Monna, N. Marcucci, F. Aguilar, M. Plóciennik, J. M. D. Lucas, G. Aloisio
In the context of the EU H2020 INDIGO-DataCloud project several use case on large scale scientific data analysis regarding different research communities have been implemented. All of them require the availability of large amount of data related to either output of simulations or observed data from sensors and need scientific (big) data solutions to run data analysis experiments. More specifically, the paper presents the case studies related to the following research communities: (i) the European Multidisciplinary Seafloor and water column Observatory (INGV-EMSO), (ii) the Large Binocular Telescope, (iii) LifeWatch, and (iv) the European Network for Earth System Modelling (ENES).
{"title":"Big Data Analytics on Large-Scale Scientific Datasets in the INDIGO-DataCloud Project","authors":"S. Fiore, Cosimo Palazzo, Alessandro D'Anca, D. Elia, E. Londero, C. Knapic, S. Monna, N. Marcucci, F. Aguilar, M. Plóciennik, J. M. D. Lucas, G. Aloisio","doi":"10.1145/3075564.3078884","DOIUrl":"https://doi.org/10.1145/3075564.3078884","url":null,"abstract":"In the context of the EU H2020 INDIGO-DataCloud project several use case on large scale scientific data analysis regarding different research communities have been implemented. All of them require the availability of large amount of data related to either output of simulations or observed data from sensors and need scientific (big) data solutions to run data analysis experiments. More specifically, the paper presents the case studies related to the following research communities: (i) the European Multidisciplinary Seafloor and water column Observatory (INGV-EMSO), (ii) the Large Binocular Telescope, (iii) LifeWatch, and (iv) the European Network for Earth System Modelling (ENES).","PeriodicalId":398898,"journal":{"name":"Proceedings of the Computing Frontiers Conference","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132813437","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}
Matthias Carnein, Dennis Assenmacher, H. Trautmann
Analysing streaming data has received considerable attention over the recent years. A key research area in this field is stream clustering which aims to recognize patterns in a possibly unbounded data stream of varying speed and structure. Over the past decades a multitude of new stream clustering algorithms have been proposed. However, to the best of our knowledge, no rigorous analysis and comparison of the different approaches has been performed. Our paper fills this gap and provides extensive experiments for a total of ten popular algorithms. We utilize a number of standard data sets of both, real and synthetic data and identify key weaknesses and strengths of the existing algorithms.
{"title":"An Empirical Comparison of Stream Clustering Algorithms","authors":"Matthias Carnein, Dennis Assenmacher, H. Trautmann","doi":"10.1145/3075564.3078887","DOIUrl":"https://doi.org/10.1145/3075564.3078887","url":null,"abstract":"Analysing streaming data has received considerable attention over the recent years. A key research area in this field is stream clustering which aims to recognize patterns in a possibly unbounded data stream of varying speed and structure. Over the past decades a multitude of new stream clustering algorithms have been proposed. However, to the best of our knowledge, no rigorous analysis and comparison of the different approaches has been performed. Our paper fills this gap and provides extensive experiments for a total of ten popular algorithms. We utilize a number of standard data sets of both, real and synthetic data and identify key weaknesses and strengths of the existing algorithms.","PeriodicalId":398898,"journal":{"name":"Proceedings of the Computing Frontiers Conference","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125143516","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}
Xi Yang, Jianbin Fang, Jing Chen, Chengkun Wu, T. Tang, Kai Lu
Coordinate descent (CD) has been proved to be an effective technique for matrix factorization (MF) in recommender systems. To speed up factorizing performance, various methods of implementing parallel CDMF have been proposed to leverage modern multi-core CPUs and many-core GPUs. Existing implementations are limited in either speed or portability (constrained to certain platforms). In this paper, we present an efficient and portable CDMF solver for recommender systems. On the one hand, we diagnose the baseline implementation and observe that it lacks the awareness of the hierarchical thread organization on modern hardware and the data variance of the rating matrix. Thus, we apply the thread batching technique and the load balancing technique to achieve high performance. On the other hand, we implement the CDMF solver in OpenCL so that it can run on various platforms. Based on the architectural specifics, we customize code variants to efficiently map them to the underlying hardware. The experimental results show that our implementation performs 2x faster on dual-socket Intel Xeon CPUs and 22x faster on an NVIDIA K20c GPU than the baseline implementations. When taking the CDMF solver as a benchmark, we observe that it runs 2.4x faster on the GPU than on the CPUs, whereas it achieves competitive performance on Intel MIC against the CPUs.
{"title":"High Performance Coordinate Descent Matrix Factorization for Recommender Systems","authors":"Xi Yang, Jianbin Fang, Jing Chen, Chengkun Wu, T. Tang, Kai Lu","doi":"10.1145/3075564.3077625","DOIUrl":"https://doi.org/10.1145/3075564.3077625","url":null,"abstract":"Coordinate descent (CD) has been proved to be an effective technique for matrix factorization (MF) in recommender systems. To speed up factorizing performance, various methods of implementing parallel CDMF have been proposed to leverage modern multi-core CPUs and many-core GPUs. Existing implementations are limited in either speed or portability (constrained to certain platforms). In this paper, we present an efficient and portable CDMF solver for recommender systems. On the one hand, we diagnose the baseline implementation and observe that it lacks the awareness of the hierarchical thread organization on modern hardware and the data variance of the rating matrix. Thus, we apply the thread batching technique and the load balancing technique to achieve high performance. On the other hand, we implement the CDMF solver in OpenCL so that it can run on various platforms. Based on the architectural specifics, we customize code variants to efficiently map them to the underlying hardware. The experimental results show that our implementation performs 2x faster on dual-socket Intel Xeon CPUs and 22x faster on an NVIDIA K20c GPU than the baseline implementations. When taking the CDMF solver as a benchmark, we observe that it runs 2.4x faster on the GPU than on the CPUs, whereas it achieves competitive performance on Intel MIC against the CPUs.","PeriodicalId":398898,"journal":{"name":"Proceedings of the Computing Frontiers Conference","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126167773","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}
The trend of increasing the number of cores in a processor will lead to certain challenges, among which the fact that more cores issue more memory requests and this in turn will increase the competition, or interference, for shared resources such as the Last-Level Cache (LLC). In this work we focus on the cache interference while executing Decision Support System queries, which is a common case for a Data Center scenario. We study the co-execution of different queries from the TPC-H benchmark using the PostgreSQL DBMS system on a multicore with up to 16 cores and different LLC configurations. In addition to the working set metric, to better understand the effects of co-execution, we develop two new "personality" metrics to classify the behavior of the queries in co-execution: social and sensitive metrics. These metrics can be used to manage the cache interference and thus improve the co-execution performance of the queries.
{"title":"Using Personality Metrics to Improve Cache Interference Management in Multicore Processors","authors":"Mwaffaq Otoom, A. Jaleel, P. Trancoso","doi":"10.1145/3075564.3075591","DOIUrl":"https://doi.org/10.1145/3075564.3075591","url":null,"abstract":"The trend of increasing the number of cores in a processor will lead to certain challenges, among which the fact that more cores issue more memory requests and this in turn will increase the competition, or interference, for shared resources such as the Last-Level Cache (LLC). In this work we focus on the cache interference while executing Decision Support System queries, which is a common case for a Data Center scenario. We study the co-execution of different queries from the TPC-H benchmark using the PostgreSQL DBMS system on a multicore with up to 16 cores and different LLC configurations. In addition to the working set metric, to better understand the effects of co-execution, we develop two new \"personality\" metrics to classify the behavior of the queries in co-execution: social and sensitive metrics. These metrics can be used to manage the cache interference and thus improve the co-execution performance of the queries.","PeriodicalId":398898,"journal":{"name":"Proceedings of the Computing Frontiers Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130022811","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}
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