Distributed metadata management, administrating the distribution of metadata nodes on different metadata servers (MDS's), can substantially improve overall performance of large-scale distributed storage systems if well designed. A major difficulty confronting many metadata management schemes is the trade-off between two conflicting aspects: system load balance and metadata locality preservation. It becomes even more challenging as file access pattern inevitably varies with time. However, existing works dynamically reallocate nodes to different servers adopting history-based coarse-grained methods, failing to make timely and efficient update on distribution of nodes. In this paper, we propose an adaptive fine-grained metadata management scheme, AdaM, leveraging Deep Reinforcement Learning, to address the trade-off dilemma against time-varying access pattern. At each time step, AdaM collects environmental "states" including access pattern, the structure of namespace tree and current distribution of nodes on MDS's. Then an actor-critic network is trained to reallocate hot metadata nodes to different servers according to the observed "states". Adaptive to varying access pattern, AdaM can automatically migrate hot metadata nodes among servers to keep load balancing while maintaining metadata locality. We test AdaM on real-world data traces. Experimental results demonstrate the superiority of our proposed method over other schemes.
{"title":"AdaM","authors":"Shiyi Cao, Yuanning Gao, Xiaofeng Gao, Guihai Chen","doi":"10.1145/3337821.3337822","DOIUrl":"https://doi.org/10.1145/3337821.3337822","url":null,"abstract":"Distributed metadata management, administrating the distribution of metadata nodes on different metadata servers (MDS's), can substantially improve overall performance of large-scale distributed storage systems if well designed. A major difficulty confronting many metadata management schemes is the trade-off between two conflicting aspects: system load balance and metadata locality preservation. It becomes even more challenging as file access pattern inevitably varies with time. However, existing works dynamically reallocate nodes to different servers adopting history-based coarse-grained methods, failing to make timely and efficient update on distribution of nodes. In this paper, we propose an adaptive fine-grained metadata management scheme, AdaM, leveraging Deep Reinforcement Learning, to address the trade-off dilemma against time-varying access pattern. At each time step, AdaM collects environmental \"states\" including access pattern, the structure of namespace tree and current distribution of nodes on MDS's. Then an actor-critic network is trained to reallocate hot metadata nodes to different servers according to the observed \"states\". Adaptive to varying access pattern, AdaM can automatically migrate hot metadata nodes among servers to keep load balancing while maintaining metadata locality. We test AdaM on real-world data traces. Experimental results demonstrate the superiority of our proposed method over other schemes.","PeriodicalId":405273,"journal":{"name":"Proceedings of the 48th International Conference on Parallel Processing","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128190948","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}
We introduce Cartesian Collective Communication as sparse, collective communication defined on processes (processors) organized into d-dimensional tori or meshes. Processes specify local neighborhoods, e.g., stencil patterns, by lists of relative Cartesian coordinate offsets. The Cartesian collective operations perform data exchanges (and reductions) over the set of all neighborhoods such that each process communicates with the processes in its local neighborhood. The key requirement is that local neighborhoods must be structurally identical (isomorphic). This makes it possible for processes to compute correct, deadlock-free, efficient communication schedules for the collective operations locally without any interaction with other processes. Cartesian Collective Communication substantially extends collective neighborhood communication on Cartesian communicators as defined by the MPI standard, and is a restricted form of neighborhood collective communication on general, distributed graph topologies. We show that the restriction to isomorphic neighborhoods permits communication improvements beyond what is possible for unrestricted graph topologies by presenting non-trivial message-combining algorithms that reduce communication latency for Cartesian alltoall and allgather collective operations. For both types of communication, the required communication schedules can be computed in linear time in the size of the input neighborhood. Our benchmarks show that we can, for small data block sizes, substantially outperform the general MPI neighborhood collectives implementing the same communication pattern. We discuss different possibilities for supporting Cartesian Collective Communication in MPI. Our library is implemented on top of MPI and uses the same signatures for the collective communication operations as the MPI (neighborhood) collectives. Our implementation requires essentially only one single, new communicator creation function, but even this might not be needed for implementation in an MPI library.
{"title":"Cartesian Collective Communication","authors":"J. Träff, S. Hunold","doi":"10.1145/3337821.3337848","DOIUrl":"https://doi.org/10.1145/3337821.3337848","url":null,"abstract":"We introduce Cartesian Collective Communication as sparse, collective communication defined on processes (processors) organized into d-dimensional tori or meshes. Processes specify local neighborhoods, e.g., stencil patterns, by lists of relative Cartesian coordinate offsets. The Cartesian collective operations perform data exchanges (and reductions) over the set of all neighborhoods such that each process communicates with the processes in its local neighborhood. The key requirement is that local neighborhoods must be structurally identical (isomorphic). This makes it possible for processes to compute correct, deadlock-free, efficient communication schedules for the collective operations locally without any interaction with other processes. Cartesian Collective Communication substantially extends collective neighborhood communication on Cartesian communicators as defined by the MPI standard, and is a restricted form of neighborhood collective communication on general, distributed graph topologies. We show that the restriction to isomorphic neighborhoods permits communication improvements beyond what is possible for unrestricted graph topologies by presenting non-trivial message-combining algorithms that reduce communication latency for Cartesian alltoall and allgather collective operations. For both types of communication, the required communication schedules can be computed in linear time in the size of the input neighborhood. Our benchmarks show that we can, for small data block sizes, substantially outperform the general MPI neighborhood collectives implementing the same communication pattern. We discuss different possibilities for supporting Cartesian Collective Communication in MPI. Our library is implemented on top of MPI and uses the same signatures for the collective communication operations as the MPI (neighborhood) collectives. Our implementation requires essentially only one single, new communicator creation function, but even this might not be needed for implementation in an MPI library.","PeriodicalId":405273,"journal":{"name":"Proceedings of the 48th International Conference on Parallel Processing","volume":"27 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114014761","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}
Influence Maximization (IM) has been extensively applied to many fields, and the viral marketing in today's online social networks (OSNs) is one of the most famous applications, where a group of seed users are selected to activate more users in a distributed cascading fashion. Many prior work explore the IM problem based on the assumption of given budget. However, the budget assumption does not hold in many practical scenarios, since companies might have no sufficient prior knowledge about the market. Moreover, companies prefer a moderately controllable viral marketing that allows them to adjust marketing decision according to the market reaction. In this paper, we propose a new problem, called Controllable social influence maximization (Cosin), to find a set of seed users inside a controllable scope to maximize the benefit given an expected return on investment (ROI). Like the IM problem, the Cosin problem is also NP-hard. We present a distributed multi-hop based framework for the influence estimation, and design a (1/2 + ϵ)-approximate algorithm based on the proposed framework. Moreover, we further present a distributed implementation to accelerate the execution of algorithm for large-scale social networks. Extensive experiments with a billion-scale social network indicate that the proposed algorithms outperform state-of-the-art algorithms in both benefit and running time.
{"title":"Cosin","authors":"Jingya Zhou, Jianxi Fan, Jin Wang","doi":"10.1145/3337821.3337858","DOIUrl":"https://doi.org/10.1145/3337821.3337858","url":null,"abstract":"Influence Maximization (IM) has been extensively applied to many fields, and the viral marketing in today's online social networks (OSNs) is one of the most famous applications, where a group of seed users are selected to activate more users in a distributed cascading fashion. Many prior work explore the IM problem based on the assumption of given budget. However, the budget assumption does not hold in many practical scenarios, since companies might have no sufficient prior knowledge about the market. Moreover, companies prefer a moderately controllable viral marketing that allows them to adjust marketing decision according to the market reaction. In this paper, we propose a new problem, called Controllable social influence maximization (Cosin), to find a set of seed users inside a controllable scope to maximize the benefit given an expected return on investment (ROI). Like the IM problem, the Cosin problem is also NP-hard. We present a distributed multi-hop based framework for the influence estimation, and design a (1/2 + ϵ)-approximate algorithm based on the proposed framework. Moreover, we further present a distributed implementation to accelerate the execution of algorithm for large-scale social networks. Extensive experiments with a billion-scale social network indicate that the proposed algorithms outperform state-of-the-art algorithms in both benefit and running time.","PeriodicalId":405273,"journal":{"name":"Proceedings of the 48th International Conference on Parallel Processing","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130358346","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}
Modern architectures provide hardware memory prefetching capabilities which can be configured at runtime. While hardware prefetching can provide substantial performance improvements for many programs, prefetching can also increase contention for shared resources such as last-level cache and memory bandwidth. In turn, this contention can degrade performance in multi-core workloads. In this paper, we model fine-grained hardware prefetcher control as a contextual bandit, and propose a framework for learning prefetcher control policies which adjust hardware prefetching usage at runtime according to workload performance behavior. We train our policies on profiling data, wherein hardware memory prefetchers are enabled or disabled randomly at regular intervals over the course of a workload's execution. The learned prefetcher control policies provide up to a 4.3% average performance improvement over a set of memory bandwidth intensive workloads.
{"title":"Machine Learning for Fine-Grained Hardware Prefetcher Control","authors":"Jason Hiebel, Laura E. Brown, Zhenlin Wang","doi":"10.1145/3337821.3337854","DOIUrl":"https://doi.org/10.1145/3337821.3337854","url":null,"abstract":"Modern architectures provide hardware memory prefetching capabilities which can be configured at runtime. While hardware prefetching can provide substantial performance improvements for many programs, prefetching can also increase contention for shared resources such as last-level cache and memory bandwidth. In turn, this contention can degrade performance in multi-core workloads. In this paper, we model fine-grained hardware prefetcher control as a contextual bandit, and propose a framework for learning prefetcher control policies which adjust hardware prefetching usage at runtime according to workload performance behavior. We train our policies on profiling data, wherein hardware memory prefetchers are enabled or disabled randomly at regular intervals over the course of a workload's execution. The learned prefetcher control policies provide up to a 4.3% average performance improvement over a set of memory bandwidth intensive workloads.","PeriodicalId":405273,"journal":{"name":"Proceedings of the 48th International Conference on Parallel Processing","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128817438","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}
Ana Gainaru, Guillaume Pallez, Hongyang Sun, P. Raghavan
New emerging fields are developing a growing number of large-scale applications with heterogeneous, dynamic and data-intensive requirements that put a high emphasis on productivity and thus are not tuned to run efficiently on today's high performance computing (HPC) systems. Some of these applications, such as neuroscience workloads and those that use adaptive numerical algorithms, develop modeling and simulation workflows with stochastic execution times and unpredictable resource requirements. When they are deployed on current HPC systems using existing resource management solutions, it can result in loss of efficiency for the users and decrease in effective system utilization for the platform providers. In this paper, we consider the current HPC scheduling model and describe the challenge it poses for stochastic applications due to the strict requirement in its job deployment policies. To address the challenge, we present speculative scheduling techniques that adapt the resource requirements of a stochastic application on-the-fly, based on its past execution behavior instead of relying on estimates given by the user. We focus on improving the overall system utilization and application response time without disrupting the current HPC scheduling model or the application development process. Our solution can operate alongside existing HPC batch schedulers without interfering with their usage modes. We show that speculative scheduling can improve the system utilization and average application response time by 25-30% compared to the classical HPC approach.
{"title":"Speculative Scheduling for Stochastic HPC Applications","authors":"Ana Gainaru, Guillaume Pallez, Hongyang Sun, P. Raghavan","doi":"10.1145/3337821.3337890","DOIUrl":"https://doi.org/10.1145/3337821.3337890","url":null,"abstract":"New emerging fields are developing a growing number of large-scale applications with heterogeneous, dynamic and data-intensive requirements that put a high emphasis on productivity and thus are not tuned to run efficiently on today's high performance computing (HPC) systems. Some of these applications, such as neuroscience workloads and those that use adaptive numerical algorithms, develop modeling and simulation workflows with stochastic execution times and unpredictable resource requirements. When they are deployed on current HPC systems using existing resource management solutions, it can result in loss of efficiency for the users and decrease in effective system utilization for the platform providers. In this paper, we consider the current HPC scheduling model and describe the challenge it poses for stochastic applications due to the strict requirement in its job deployment policies. To address the challenge, we present speculative scheduling techniques that adapt the resource requirements of a stochastic application on-the-fly, based on its past execution behavior instead of relying on estimates given by the user. We focus on improving the overall system utilization and application response time without disrupting the current HPC scheduling model or the application development process. Our solution can operate alongside existing HPC batch schedulers without interfering with their usage modes. We show that speculative scheduling can improve the system utilization and average application response time by 25-30% compared to the classical HPC approach.","PeriodicalId":405273,"journal":{"name":"Proceedings of the 48th International Conference on Parallel Processing","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123811126","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}
Xin Chen, Dmytro Konobrytskyi, Thomas M. Tucker, T. Kurfess, R. Vuduc
This paper presents a new and more work-efficient parallel method to speed up a class of three-dimensional collision detection (CD) problems, which arise, for instance, in computer numerical control (CNC) milling. Given two objects, one enclosed by a bounding volume and the other represented by a voxel model, we wish to determine all possible orientations of the bounded object around a given point that do not cause collisions. Underlying most CD methods are 3 types of geometrical operations that are bottlenecks: decompositions, rotations, and projections. Our proposed approach, which we call the aggressive inaccessible cone angle (AICA) method, simplifies these operations and, empirically, can prune as much as 99% of the intersection tests that would otherwise be required and improve load balance. We validate our techniques by implementing a parallel version of AICA in SculptPrint, a state-of-the-art computer-aided manufacturing (CAM) application used CNC milling, for GPU platforms. Experimental results using 4 CAM benchmarks show that AICA can be over 23× faster than a baseline method that does not prune projections, and can check collisions for 4096 angle orientations in an object represented by 27 million voxels in less than 18 milliseconds on a GPU.
{"title":"Faster parallel collision detection at high resolution for CNC milling applications","authors":"Xin Chen, Dmytro Konobrytskyi, Thomas M. Tucker, T. Kurfess, R. Vuduc","doi":"10.1145/3337821.3337838","DOIUrl":"https://doi.org/10.1145/3337821.3337838","url":null,"abstract":"This paper presents a new and more work-efficient parallel method to speed up a class of three-dimensional collision detection (CD) problems, which arise, for instance, in computer numerical control (CNC) milling. Given two objects, one enclosed by a bounding volume and the other represented by a voxel model, we wish to determine all possible orientations of the bounded object around a given point that do not cause collisions. Underlying most CD methods are 3 types of geometrical operations that are bottlenecks: decompositions, rotations, and projections. Our proposed approach, which we call the aggressive inaccessible cone angle (AICA) method, simplifies these operations and, empirically, can prune as much as 99% of the intersection tests that would otherwise be required and improve load balance. We validate our techniques by implementing a parallel version of AICA in SculptPrint, a state-of-the-art computer-aided manufacturing (CAM) application used CNC milling, for GPU platforms. Experimental results using 4 CAM benchmarks show that AICA can be over 23× faster than a baseline method that does not prune projections, and can check collisions for 4096 angle orientations in an object represented by 27 million voxels in less than 18 milliseconds on a GPU.","PeriodicalId":405273,"journal":{"name":"Proceedings of the 48th International Conference on Parallel Processing","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114659781","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}
In distributed file systems, distributed metadata management can be considered as a mapping problem, i.e., how to effectively map the metadata namespace tree to multiple metadata servers (MDS's). In general, all traditional distributed metadata management schemes simply presume a rigid mapping function, thus failing to adaptively meet the requirements of different applications. To better take advantage of the current distribution of the metadata, in this exploratory paper, we present the first machine learning based model called DeepHash, which leverages the deep neural network to learn a locality preserving hashing (LPH) mapping. To help learn a good position relationship of metadata nodes in the namespace tree, we first present a metadata representation strategy. Due to the absence of training labels, i.e., the hash values of metadata nodes, we design two kinds of loss functions with distinctive characters to train DeepHash respectively, including a pair loss and a triplet loss, and introduce some sampling strategies for these two approaches. We conduct extensive experiments on Amazon EC2 platform to compare the performance of DeepHash with traditional and state-of-the-art schemes. The results demonstrate that DeepHash can preserve the metadata locality well while maintaining a high load balancing, which denotes the effectiveness and efficiency of DeepHash.
{"title":"DeepHash","authors":"Yuanning Gao, Xiaofeng Gao, Guihai Chen","doi":"10.1145/3337821.3337924","DOIUrl":"https://doi.org/10.1145/3337821.3337924","url":null,"abstract":"In distributed file systems, distributed metadata management can be considered as a mapping problem, i.e., how to effectively map the metadata namespace tree to multiple metadata servers (MDS's). In general, all traditional distributed metadata management schemes simply presume a rigid mapping function, thus failing to adaptively meet the requirements of different applications. To better take advantage of the current distribution of the metadata, in this exploratory paper, we present the first machine learning based model called DeepHash, which leverages the deep neural network to learn a locality preserving hashing (LPH) mapping. To help learn a good position relationship of metadata nodes in the namespace tree, we first present a metadata representation strategy. Due to the absence of training labels, i.e., the hash values of metadata nodes, we design two kinds of loss functions with distinctive characters to train DeepHash respectively, including a pair loss and a triplet loss, and introduce some sampling strategies for these two approaches. We conduct extensive experiments on Amazon EC2 platform to compare the performance of DeepHash with traditional and state-of-the-art schemes. The results demonstrate that DeepHash can preserve the metadata locality well while maintaining a high load balancing, which denotes the effectiveness and efficiency of DeepHash.","PeriodicalId":405273,"journal":{"name":"Proceedings of the 48th International Conference on Parallel Processing","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114721992","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}
Virtualized network functions (VNFs) enable software applications to replace traditional middleboxes, which is more flexible and scalable in the network service provision. This paper focuses on ensuring Service Availability via Failure Elimination (SAFE) using VNF scaling, that is, given the resource requirements of VNF instances, finding an optimal and robust instance consolidation strategy, which can recover from one instance failure quickly. To address the above problem, we present a framework based on rounding and dynamic programming. First, we discretize the range of resource requirements for VNF instances deployment into several sub-ranges, so that the number of instance types becomes a constant. Second, we further reduce the number of instance types by gathering several small instances into a bigger one. Third, we propose an algorithm built on dynamic programming to solve the instance consolidation problem with a limited number of instance types. We set up a testbed to profile the functional relationship between resource and throughput for different types of VNF instances, and conduct simulations to validate our theoretical results according to profiling results. The simulation results show that our algorithm outperforms the standby deployment model by 27.33% on average in terms of the number of servers required. Furthermore, SAFE has marginal overhead, around 7.22%, compared to instance consolidation strategy without VNF backup consideration.
{"title":"SAFE: Service Availability via Failure Elimination Through VNF Scaling","authors":"Rui Xia, Haipeng Dai, Jiaqi Zheng, Rong Gu, Xiaoyu Wang, Guihai Chen","doi":"10.1145/3337821.3337832","DOIUrl":"https://doi.org/10.1145/3337821.3337832","url":null,"abstract":"Virtualized network functions (VNFs) enable software applications to replace traditional middleboxes, which is more flexible and scalable in the network service provision. This paper focuses on ensuring Service Availability via Failure Elimination (SAFE) using VNF scaling, that is, given the resource requirements of VNF instances, finding an optimal and robust instance consolidation strategy, which can recover from one instance failure quickly. To address the above problem, we present a framework based on rounding and dynamic programming. First, we discretize the range of resource requirements for VNF instances deployment into several sub-ranges, so that the number of instance types becomes a constant. Second, we further reduce the number of instance types by gathering several small instances into a bigger one. Third, we propose an algorithm built on dynamic programming to solve the instance consolidation problem with a limited number of instance types. We set up a testbed to profile the functional relationship between resource and throughput for different types of VNF instances, and conduct simulations to validate our theoretical results according to profiling results. The simulation results show that our algorithm outperforms the standby deployment model by 27.33% on average in terms of the number of servers required. Furthermore, SAFE has marginal overhead, around 7.22%, compared to instance consolidation strategy without VNF backup consideration.","PeriodicalId":405273,"journal":{"name":"Proceedings of the 48th International Conference on Parallel Processing","volume":"75 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116470400","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}
Deduplication, as a space-saving technology, is widely deployed in the flash-based storage systems to address the capacity and endurance limitations of flash devices. In this paper, we find that deduplication changes the physical data layout, which raises the chances of the uneven read distribution. This uneven read distribution not only increases the access contention but also deteriorates the read parallelism, thus leading to the read performance degradation. To solve this issue, we propose an efficient read-leveling data distribution scheme (RLDDS), which scatters the highly-duplicated data into different parallel units, to improve the read performance for SSDs with deduplication for access-intensive workloads. RLDDS writes data into a parallel unit with lower potential read-hotness to balance the read distribution among all the parallel units. Extensive experimental results show that RLDDS effectively improves the read performance by up to 21.61% compared to deduplication with the conventional dynamic data allocation scheme. Additional benefits of RLDDS include the promoted write performance (up to 23.69%) in access-intensive workloads and the overall system performance improvement (up to 18.22%) with the same write traffic reduction.
{"title":"A Read-leveling Data Distribution Scheme for Promoting Read Performance in SSDs with Deduplication","authors":"Mengting Lu, F. Wang, D. Feng, Yuchong Hu","doi":"10.1145/3337821.3337884","DOIUrl":"https://doi.org/10.1145/3337821.3337884","url":null,"abstract":"Deduplication, as a space-saving technology, is widely deployed in the flash-based storage systems to address the capacity and endurance limitations of flash devices. In this paper, we find that deduplication changes the physical data layout, which raises the chances of the uneven read distribution. This uneven read distribution not only increases the access contention but also deteriorates the read parallelism, thus leading to the read performance degradation. To solve this issue, we propose an efficient read-leveling data distribution scheme (RLDDS), which scatters the highly-duplicated data into different parallel units, to improve the read performance for SSDs with deduplication for access-intensive workloads. RLDDS writes data into a parallel unit with lower potential read-hotness to balance the read distribution among all the parallel units. Extensive experimental results show that RLDDS effectively improves the read performance by up to 21.61% compared to deduplication with the conventional dynamic data allocation scheme. Additional benefits of RLDDS include the promoted write performance (up to 23.69%) in access-intensive workloads and the overall system performance improvement (up to 18.22%) with the same write traffic reduction.","PeriodicalId":405273,"journal":{"name":"Proceedings of the 48th International Conference on Parallel Processing","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115916533","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}
Tao Wang, Nikhil Jain, D. Beckingsale, David Boehme, F. Mueller, T. Gamblin
The de facto compilation model for production software compiles all modules of a target program with a single set of compilation flags, typically 02 or 03. Such a per-program compilation strategy may yield sub-optimal executables since programs often have multiple hot loops with diverse code structures and may be better optimized with a per-region compilation model that assembles an optimized executable by combining the best per-region code variants. In this paper, we demonstrate that a naïve greedy approach to per-region compilation often degrades performance in comparison to the 03 baseline. To overcome this problem, we contribute a novel per-loop compilation framework, FuncyTuner, which employs lightweight profiling to collect per-loop timing information, and then utilizes a space-focusing technique to construct a performant executable. Experimental results show that FuncyTuner can reliably improve performance of modern scientific applications on several multi-core architectures by 9.2% to 12.3% and 4.5% to 10.7%(geometric mean, up to 22% on certain program) in comparison to the 03 baseline and prior work, respectively.
{"title":"FuncyTuner","authors":"Tao Wang, Nikhil Jain, D. Beckingsale, David Boehme, F. Mueller, T. Gamblin","doi":"10.1145/3337821.3337842","DOIUrl":"https://doi.org/10.1145/3337821.3337842","url":null,"abstract":"The de facto compilation model for production software compiles all modules of a target program with a single set of compilation flags, typically 02 or 03. Such a per-program compilation strategy may yield sub-optimal executables since programs often have multiple hot loops with diverse code structures and may be better optimized with a per-region compilation model that assembles an optimized executable by combining the best per-region code variants. In this paper, we demonstrate that a naïve greedy approach to per-region compilation often degrades performance in comparison to the 03 baseline. To overcome this problem, we contribute a novel per-loop compilation framework, FuncyTuner, which employs lightweight profiling to collect per-loop timing information, and then utilizes a space-focusing technique to construct a performant executable. Experimental results show that FuncyTuner can reliably improve performance of modern scientific applications on several multi-core architectures by 9.2% to 12.3% and 4.5% to 10.7%(geometric mean, up to 22% on certain program) in comparison to the 03 baseline and prior work, respectively.","PeriodicalId":405273,"journal":{"name":"Proceedings of the 48th International Conference on Parallel Processing","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123044325","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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