{"title":"Performance & Energy Tradeoffs for Dependent Distributed Applications Under System-wide Power Caps","authors":"Huazhe Zhang, H. Hoffmann","doi":"10.1145/3225058.3225098","DOIUrl":"https://doi.org/10.1145/3225058.3225098","url":null,"abstract":"Large scale parallel machines are subject to system-wide power budgets (or caps). As these machines grow in capacity, they can concurrently execute dependent applications that were previously processed serially. Such application coupling saves IO and time as the applications now communicate at runtime instead of through disk. Such coupled applications are predicted to be a major workload for future exascale supercomputers; e.g., scientific simulations will execute concurrently with in situ analysis. While support for system-wide power caps has been widely studied, prior work does not consider the impact on coupled applications. We study techniques for maximizing coupled application performance under a system-wide power cap and implement them on a 26-node cluster. We compare to SLURM, a state-of-the-art job scheduler that considers power, but not coupling. The proposed techniques increase mean performance over SLURM by 7-14%. Unlike existing approaches, the proposed techniques also recognize when it is not possible to increase performance and, instead, reduce energy, achieving 18% energy reduction for a 5% performance loss. Finally, the dynamic techniques are resilient to tail behavior and system noise, improving performance in noisy environments by 30-36%.","PeriodicalId":307809,"journal":{"name":"Proceedings of the 47th International Conference on Parallel Processing","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121143845","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}
{"title":"Improving Resource Utilization through Demand Aware Process Scheduling","authors":"Brandon Nesterenko, Qing Yi, J. Rao","doi":"10.1145/3225058.3225132","DOIUrl":"https://doi.org/10.1145/3225058.3225132","url":null,"abstract":"Traditional process scheduling in the operating system focuses on high CPU utilization while achieving fairness among the processes. However, this can lead to an inefficient usage of other hardware resources, e.g., the caches, which have limited capacity and is a scarce resource on most systems. This paper extends a traditional operating system scheduler to schedule processes more efficiently against hardware resources. Through the introduction of a new concept, a progress period, which models the variation of resource access characteristics during application execution, our scheduling extension dynamically monitors the changes in resource access behavior of each process being scheduled, tracks their collective usage of hardware resources, and schedules the processes to decrease overall system power consumption without compromising performance. Testing this scheduling system on programs on an Intel(R) Xeon(R) E5-2420 CPU with twelve kernels from the BLAS suite and five applications from the SPLASH-2 benchmark suite yielded a 48% maximum decrease in system energy consumption (average 12%), and a 1.88x maximum increase in application performance (average 1.16x).","PeriodicalId":307809,"journal":{"name":"Proceedings of the 47th International Conference on Parallel Processing","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131172786","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}
{"title":"NFV Middlebox Placement with Balanced Set-up Cost and Bandwidth Consumption","authors":"Yang Chen, Jie Wu","doi":"10.1145/3225058.3225068","DOIUrl":"https://doi.org/10.1145/3225058.3225068","url":null,"abstract":"Network Function Virtualization (NFV) changes the way that we implement network services, or middleboxes, from expensive hardwares to software functions. These software middleboxes, also called Virtual Network Functions (VNFs), run on switch-connected commodity servers. Efficiently placing such middleboxes is challenging because of their traffic-changing effects and dependency relations. Private (used by one single flow) middleboxes can save more link bandwidth resources while shared (used by multiple flows) middleboxes cut down server resource expenses. This paper formulates the resource usage trade-off between bandwidth consumption and cost of middlebox placement as a combined cost minimization problem. After proving the NP-hardness of our problem in general topologies, we narrow down to a specific kind of topology: tree-structured networks. We study two kinds of constraints: traffic-chaining ratio and middlebox dependency relations. With homogeneous flows, we propose optimal greedy algorithms for the placement of a single middlebox first, and then multiple middleboxes without order. We also introduce a dynamic programming algorithm for the placement of a totally-ordered middlebox set. A performance-guaranteed algorithm is designed to handle heterogeneous flows. Extensive simulations are conducted to evaluate the performance of our proposed algorithms in various scenarios.","PeriodicalId":307809,"journal":{"name":"Proceedings of the 47th International Conference on Parallel Processing","volume":"115 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132072852","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}
J. Hückelheim, P. Hovland, S. Narayanan, Paulius Velesko
{"title":"Vectorised Computation of Diverging Ensembles","authors":"J. Hückelheim, P. Hovland, S. Narayanan, Paulius Velesko","doi":"10.1145/3225058.3225138","DOIUrl":"https://doi.org/10.1145/3225058.3225138","url":null,"abstract":"Ensemble computations are used to evaluate a function for multiple inputs, for example in uncertainty quantification. Embedded ensemble computations perform several evaluations within the same program, often enabling a reduced overall runtime by exploiting vectorisation and parallelisation opportunities that are not present in individual ensemble members. This is challenging if members take different control flow paths. We present a source-to-source transformation that turns a given C program into an embedded ensemble program that computes members in a single-instruction-multiple-data fashion using OpenMP SIMD pragmas. We use techniques from whole-function vectorisation, achieving effective vectorisation for moderate amounts of branch divergence, particularly on processors with masked instructions such as recent Xeon Phi or Skylake processors with AVX-512.","PeriodicalId":307809,"journal":{"name":"Proceedings of the 47th International Conference on Parallel Processing","volume":"252 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114563745","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}
{"title":"DAG-SFC: Minimize the Embedding Cost of SFC with Parallel VNFs","authors":"Xu Lin, Deke Guo, Yulong Shen, Guoming Tang, Bangbang Ren","doi":"10.1145/3225058.3225111","DOIUrl":"https://doi.org/10.1145/3225058.3225111","url":null,"abstract":"Network Function Virtualization (NFV) is an emerging technology, which enables service agility, flexibility and cost reduction by replacing traditional hardware middleboxes with Virtual Network Functions (VNFs) running on general-purpose servers. Service Function Chain (SFC) constitutes an end-to-end service by organizing a series of VNFs in a specific order. Particularly, hybrid SFC (SFC with parallel VNFs) is proposed to much reduce the traffic delay in sequential SFCs. Nevertheless, how to strategically select VNF instances and links in hybrid SFC embedding remains an open problem. In this paper, we target at the cost minimization and address the optimal hybrid SFC embedding problem. Specifically, we first develop a novel abstraction model for the hybrid SFC with Directed Acyclic Graph (DAG), which helps convert diverse hybrid SFCs to the standardized DAG-SFC form. Then, we formulate the optimal DAG-SFC embedding problem as an integer optimization model and propose a greedy method (called BBE) to solve the NP-hard problem. MBBE method is developed upon BBE method to further cut down the computation complexity in model solving. Extensive simulation results demonstrate the effectiveness of our approach for cost reduction in hybrid SFC embedding.","PeriodicalId":307809,"journal":{"name":"Proceedings of the 47th International Conference on Parallel Processing","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116220465","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}
{"title":"Efficient Runtime Support for a Partitioned Global Logical Address Space","authors":"D. B. Larkins, John Snyder, James Dinan","doi":"10.1145/3225058.3225092","DOIUrl":"https://doi.org/10.1145/3225058.3225092","url":null,"abstract":"Many HPC applications have successfully applied Partitioned Global Address Space (PGAS) parallel programming models to efficiently manage shared data that is distributed across multiple nodes in a distributed memory system. However, while the flat addressing model provided by PGAS systems is effective for regular array data, it renders such systems difficult to use with loosely-structured or sparse data. This work proposes a logically addressed PGLAS model that naturally supports a variety of data models through the automatic mapping of an application-defined key space onto the underlying distributed memory system. We present an efficient implementation of the PGLAS model built atop a parallel distributed hash table (PDHT) and demonstrate that this model is amenable to offloading using the Portals 4 network programming interface. We demonstrate the effectiveness of PDHT using representative applications from the computational chemistry and genomics domains. Results indicate that PGLAS models such as PDHT provide a promising new method for parallelizing applications with non-regular data.","PeriodicalId":307809,"journal":{"name":"Proceedings of the 47th International Conference on Parallel Processing","volume":"204 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133300149","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}
R. Kesavan, Matthew Curtis-Maury, Mrinal K. Bhattacharjee
{"title":"Efficient Search for Free Blocks in the WAFL File System","authors":"R. Kesavan, Matthew Curtis-Maury, Mrinal K. Bhattacharjee","doi":"10.1145/3225058.3225072","DOIUrl":"https://doi.org/10.1145/3225058.3225072","url":null,"abstract":"The WAFL® write allocator is responsible for assigning blocks on persistent storage to data in a way that maximizes both write throughput to the storage media and subsequent read performance of data. The ability to quickly and efficiently guide the write allocator toward desirable regions of available free space is critical to achieving that goal. This ability is influenced by several factors, such as any underlying RAID geometry, media-specific attributes such as erase-block size of solid state drives or zone size of shingled magnetic hard drives, and free space fragmentation. This paper presents and evaluates the techniques used by the WAFL write allocator to efficiently find regions of free space.","PeriodicalId":307809,"journal":{"name":"Proceedings of the 47th International Conference on Parallel Processing","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133925242","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}
{"title":"A Distributed Infomap Algorithm for Scalable and High-Quality Community Detection","authors":"Jianping Zeng, Hongfeng Yu","doi":"10.1145/3225058.3225137","DOIUrl":"https://doi.org/10.1145/3225058.3225137","url":null,"abstract":"Community detection is essential to various graph analysis applications. Infomap is a graph clustering algorithm capable of achieving high-quality communities. However, it remains a very challenging problem to effectively apply Infomap on large graphs. By analyzing communication and workload patterns of Infomap and leveraging a distributed delegate partitioning and distribution method, we develop a new heuristic strategy to carefully coordinate the community constitution from the vertices of a graph in a distributed environment, and achieve the convergence of the distributed clustering algorithm. We have implemented our optimized algorithm using MPI (Message Passing Interface), which can be easily employed or extended to massively distributed computing systems. We analyze the correctness of our algorithm, and conduct an intensive experimental study to investigate the communication and computation cost of our distributed algorithm, which has not shown in previous work. The results demonstrate the scalability and the correctness of our distributed Infomap algorithm with large-scale real-world datasets.","PeriodicalId":307809,"journal":{"name":"Proceedings of the 47th International Conference on Parallel Processing","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123985090","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}
{"title":"C-Graph: A Highly Efficient Concurrent Graph Reachability Query Framework","authors":"Li Zhou, Ren Chen, Yinglong Xia, R. Teodorescu","doi":"10.1145/3225058.3225136","DOIUrl":"https://doi.org/10.1145/3225058.3225136","url":null,"abstract":"Many big data analytics applications explore a set of related entities, which are naturally modeled as graph. However, graph processing is notorious for its performance challenges due to random data access patterns, especially for large data volumes. Solving these challenges is critical to the performance of industry-scale applications. In contrast to most prior works, which focus on accelerating a single graph processing task, in industrial practice we consider multiple graph processing tasks running concurrently, such as a group of queries issued simultaneously to the same graph. In this paper, we present an edge-set based graph traversal framework called C-Graph (i.e. Concurrent Graph), running on a distributed infrastructure, that achieves both high concurrency and efficiency for k-hop reachability queries. The proposed framework maintains global vertex states to facilitate graph traversals, and supports both synchronous and asynchronous communication. In this study, we decompose a set of graph processing tasks into local traversals and analyze their performance on C-Graph. More specifically, we optimize the organization of the physical edge-set and explore the shared subgraphs. We experimentally show that our proposed framework outperforms several baseline methods.","PeriodicalId":307809,"journal":{"name":"Proceedings of the 47th International Conference on Parallel Processing","volume":"103 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115520544","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}
{"title":"Cache Assisted Randomized Sharing Counters in Network Measurement","authors":"Qian Liu, Haipeng Dai, A. Liu, Qi Li, Xiaoyu Wang, Jiaqi Zheng","doi":"10.1145/3225058.3225078","DOIUrl":"https://doi.org/10.1145/3225058.3225078","url":null,"abstract":"This paper proposes a new counter architecture for network measurement called Cache Assisted and randomizEd ShAring counteRs (CAESAR). One of the greatest challenges for per-flow traffic measurement is designing an online measurement module to keep up with the rapid growth of link speed. To address this challenge, we use a fast on-chip memory as the cache before the slow off-chip SRAM counters, thereby decreasing the accesses per flow to off-chip counters to improve time efficiency without any packet loss. We use randomized sharing counters among multiple flows in SRAM to achieve a compact data structure with high storage efficiency. By removing the impact from other flows sharing counters with a specific flow, we theoretically analyze the expectation and confidence interval of its estimated flow size accurately. In this paper, we use the real-world network traces for software simulations and FPGA experiments on the Xilinx Virtex-7 FPGA chip to validate our theoretical findings. The results show that CAESAR is up to 92.4% and 90% faster than prior work CASE and RCS respectively, and CAESAR reduces the average relative error of CASE and RCS by more than half.","PeriodicalId":307809,"journal":{"name":"Proceedings of the 47th International Conference on Parallel Processing","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115639258","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}