2018 30th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)最新文献

MLNoC: A Machine Learning Based Approach to NoC Design MLNoC:基于机器学习的NoC设计方法

2018 30th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD) Pub Date : 2018-09-01 DOI: 10.1109/CAHPC.2018.8645914

N. Rao, Akshay Ramachandran, Amish Shah

{"title":"MLNoC: A Machine Learning Based Approach to NoC Design","authors":"N. Rao, Akshay Ramachandran, Amish Shah","doi":"10.1109/CAHPC.2018.8645914","DOIUrl":"https://doi.org/10.1109/CAHPC.2018.8645914","url":null,"abstract":"Modern System on Chips (SoCs) are becoming increasingly complex with a growing number of CPUs, caches, accelerators, memory and I/O subsystems. For such designs, a packet based distributed networks-on-chip (NoCs) interconnect can provide scalability, performance and efficiency. However, the design of such a NoC involves optimizing a large number of variables such as topology, routing choices, arbitration and quality of service (QoS) policies, buffer sizes, and deadlock avoidance policies. Widely varying die sizes, power, floorplan and performance constraints across a variety of different market segments, ranging from high-end servers to low-end IoT devices, impose additional design challenges. In this paper we demonstrate that there is a strong correlation between SoC characteristics and good NoC design practices. However this correlation is highly non-linear and multidimensional, with dimensions indicative of the features of the SoC, design goals and properties of the NoC. This results in a high-dimensional NoC design space and complex search process which is inefficient to solve with classic algorithms. Using a variety of real SoCs and training data sets, we demonstrate that a machine learning (ML) based approach yields near-optimal NoC designs quickly. We determine a number of SoC and NoC features, describe reduction methods, and also show that a multi-model approach yields better designs. We demonstrate that for a wide variety of SoCs, ML based NoC designs are far superior to those designed and optimized manually over years on almost all quality metrics.","PeriodicalId":307747,"journal":{"name":"2018 30th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125498329","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: 5

Exploiting Limited Access Distance for Kernel Fusion Across the Stages of Explicit One-Step Methods on GPUs 利用有限访问距离实现gpu上的显式一步法跨阶段核融合

2018 30th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD) Pub Date : 2018-09-01 DOI: 10.1109/CAHPC.2018.8645892

Matthias Korch, Tim Werner

引用次数: 3

Optimization of a Sparse Grid-Based Data Mining Kernel for Architectures Using AVX-512 基于AVX-512架构的稀疏网格数据挖掘内核优化

2018 30th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD) Pub Date : 2018-09-01 DOI: 10.1109/CAHPC.2018.8645913

Paul-Cristian Sarbu, H. Bungartz

引用次数: 1

Exploring the Potential of Next Generation Software-Defined in Memory Frameworks 探索下一代软件定义内存框架的潜力

2018 30th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD) Pub Date : 2018-09-01 DOI: 10.1109/CAHPC.2018.8645858

Shouwei Chen, I. Rodero

{"title":"Exploring the Potential of Next Generation Software-Defined in Memory Frameworks","authors":"Shouwei Chen, I. Rodero","doi":"10.1109/CAHPC.2018.8645858","DOIUrl":"https://doi.org/10.1109/CAHPC.2018.8645858","url":null,"abstract":"As in-memory data analytics become increasingly important in a wide range of domains, the ability to develop large-scale and sustainable platforms faces significant challenges related to storage latency and memory size constraints. These challenges can be resolved by adopting new and effective formulations and novel architectures such as software-defined infrastructure. This paper investigates the key issue of data persistency for in-memory processing systems by evaluating persistence methods using different storage and memory devices for Apache Spark and the use of Alluxio. It also proposes and evaluates via simulation a Spark execution model for using disaggregated off-rack memory and non-volatile memory targeting next-generation software-defined infrastructure. Experimental results provide better understanding of behaviors and requirements for improving data persistence in current in-memory systems and provide data points to better understand requirements and design choices for next-generation software-defined infrastructure. The findings indicate that in-memory processing systems can benefit from ongoing software-defined infrastructure implementations; however current frameworks need to be enhanced appropriately to run efficiently at scale.","PeriodicalId":307747,"journal":{"name":"2018 30th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129638747","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

Exploring Self-Adaptivity Towards Performance and Energy for Time-Stepping Methods 探索时间步进方法对性能和能量的自适应性

2018 30th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD) Pub Date : 2018-09-01 DOI: 10.1109/CAHPC.2018.8645887

Natalia Kalinnik, R. Kiesel, T. Rauber, Marcel Richter, G. Rünger

引用次数: 1

DOACROSS Parallelization Based on Component Annotation and Loop-Carried Probability 基于组件标注和循环携带概率的DOACROSS并行化

2018 30th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD) Pub Date : 2018-09-01 DOI: 10.1109/CAHPC.2018.8645904

Luis Mattos, D. C. S. Lucas, Juan Salamanca, J. P. L. Carvalho, M. Pereira, G. Araújo

{"title":"DOACROSS Parallelization Based on Component Annotation and Loop-Carried Probability","authors":"Luis Mattos, D. C. S. Lucas, Juan Salamanca, J. P. L. Carvalho, M. Pereira, G. Araújo","doi":"10.1109/CAHPC.2018.8645904","DOIUrl":"https://doi.org/10.1109/CAHPC.2018.8645904","url":null,"abstract":"Although modern compilers implement many loop parallelization techniques, their application is typically restricted to loops that have no loop-carried dependences (DOALL) or that contain well-known structured dependence patterns (e.g. reduction). These restrictions preclude the parallelization of many computational intensive DOACROSS loops. In such loops, either the compiler finds at least one loop-carried dependence or it cannot prove, at compile-time, that the loop is free of such dependences, even though they might never show-up at runtime. In any case, most compilers end-up not parallelizing DOACROSS loops. This paper brings three contributions to address this problem. First, it integrates three algorithms (TLS, DOAX, and BDX) into a simple openMP clause that enables the programmer to select the best algorithm for a given loop. Second, it proposes an annotation approach to separate the sequential components of a loop, thus exposing other components to parallelization. Finally, it shows that loop-carried probability is an effective metric to decide when to use TLS or other non-speculative techniques (e.g. DOAX or BDX) to parallelize DOACROSS loops. Experimental results reveal that, for certain loops, slow-downs can be transformed in 2×speed-ups by quickly selecting the appropriate algorithm.","PeriodicalId":307747,"journal":{"name":"2018 30th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126405858","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

Assessing Time Predictability Features of ARM Big. LITTLE Multicores 评估ARM Big的时间可预测性特征。小多核

2018 30th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD) Pub Date : 2018-09-01 DOI: 10.1109/CAHPC.2018.8645925

Gabriel Fernandez, F. Cazorla, J. Abella, Sylvain Girbal

引用次数: 3

Performance Comparison of a Parallel Recommender Algorithm Across Three Hadoop-Based Frameworks 一种基于hadoop的并行推荐算法的性能比较

2018 30th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD) Pub Date : 2018-09-01 DOI: 10.1109/CAHPC.2018.8645926

Christina Diedhiou, Bryan Carpenter, A. Shafi, Soumabha Sarkar, Ramazan Esmeli, Ryan Gadsdon

{"title":"Performance Comparison of a Parallel Recommender Algorithm Across Three Hadoop-Based Frameworks","authors":"Christina Diedhiou, Bryan Carpenter, A. Shafi, Soumabha Sarkar, Ramazan Esmeli, Ryan Gadsdon","doi":"10.1109/CAHPC.2018.8645926","DOIUrl":"https://doi.org/10.1109/CAHPC.2018.8645926","url":null,"abstract":"One of the challenges our society faces is the ever increasing amount of data. Among existing platforms that address the system requirements, Hadoop is a framework widely used to store and analyze “big data”. On the human side, one of the aids to finding the things people really want is recommendation systems. This paper evaluates highly scalable parallel algorithms for recommendation systems with application to very large data sets. A particular goal is to evaluate an open source Java message passing library for parallel computing called MPJ Express, which has been integrated with Hadoop. As a demonstration we use MPJ Express to implement collaborative filtering on various data sets using the algorithm ALSWR (Alternating-Least-Squares with Weighted-λ-Regularization). We benchmark the performance and demonstrate parallel speedup on Movielens and Yahoo Music data sets, comparing our results with two other frameworks: Mahout and Spark. Our results indicate that MPJ Express implementation of ALSWR has very competitive performance and scalability in comparison with the two other frameworks.","PeriodicalId":307747,"journal":{"name":"2018 30th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121762575","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

Adaptive Partitioning for Iterated Sequences of Irregular OpenCL Kernels 不规则OpenCL核迭代序列的自适应划分

2018 30th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD) Pub Date : 2018-09-01 DOI: 10.1109/SBAC-PAD.2018.00051

Pierre Huchant, Denis Barthou, M. Counilh

引用次数: 1

Network-Aware Energy-Efficient Virtual Machine Management in Distributed Cloud Infrastructures with On-Site Photovoltaic Production 分布式云基础设施中具有网络感知的节能虚拟机管理

2018 30th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD) Pub Date : 2018-09-01 DOI: 10.1109/CAHPC.2018.8645901

Benjamin Camus, F. Dufossé, A. Blavette, M. Quinson, Anne-Cécile Orgerie

引用次数: 6