Heuristic Algorithms for Replication Transition Problem in the Grid Systems

Understanding the inherent system characteristics is crucial to the design and optimization of cloud storage system, and few studies have systematically investigated its data characteristics and access patterns. This paper presents an analysis of file system snapshot and five-month access trace of a campus cloud storage system that has been deployed on Tsinghua campus for three years. The system provides online storage and data sharing services for more than 19,000 students and 500 student groups. We report several data characteristics including file size and file type, as well as some access patterns, including read/write ratio, read-write dependency and daily traffic. We find that there are many differences between cloud storage system and traditional file systems: our cloud storage system has larger file sizes, lower read/write ratio, and smaller set of active files than those of a typical traditional file system. With a trace-driven simulation, we find that the cache efficiency can be improved by 5 times using the guidance from our observations.

The need for energy efficiency continues to grow for many classes of processors, including those for which performance remains vital. Data cache is crucial for good performance, but it also represents a significant portion of the processor's energy expenditure. We describe the implementation and use of a tagless access buffer (TAB) that greatly improves data access energy efficiency while slightly improving performance. The compiler recognizes memory reference patterns within loops and allocates these references to a TAB. This combined hardware/software approach reduces energy usage by (1) replacing many level-one data cache (L1D) accesses with accesses to the smaller, more power-efficient TAB; (2) removing the need to perform tag checks or data translation lookaside buffer (DTLB) lookups for TAB accesses; and (3) reducing DTLB lookups when transferring data between the L1D and the TAB. Accesses to the TAB occur earlier in the pipeline, and data lines are prefetched from lower memory levels, which result in a small performance improvement. In addition, we can avoid many unnecessary block transfers between other memory hierarchy levels by characterizing how data in the TAB are used. With a combined size equal to that of a conventional 32-entry register file, a four-entry TAB eliminates 40% of L1D accesses and 42% of DTLB accesses, on average. This configuration reduces data-access related energy by 35% while simultane-ously decreasing execution time by 3%.

The performance achieved by an application is limited by architectural features such as program data access and processing patterns. Parallelization approaches exhibit dissimilar performance and have a direct impact in application execution time. Additionally, developing parallel code involves additional complexity and productivity for programmers to accelerate or rewrite the program. In this paper, we present a comparative performance evaluation of a CPU, GPU, and many-core (Xeon Phi KNL) architectures for the Non-Local Means filter. We asses the effect of different data access and processing patterns in two computational optimizations developed for the aforementioned filter. We follow a top-down approach in terms of the parallelization approach chosen, starting from performance primitives as a first step to give easy drop-in acceleration and then compiler directives with frameworks such as OpenMP and OpenACC as an intermediate step to map computing tasks to the underlying hardware. Results show that both libraries and directives are effective at accelerating code with a combination of both being necessary to overcome performance bottlenecks.

Optimizing convolutional neural networks on multi-core vector accelerator

As deep learning technologies increasingly influence various aspects of human life, the emerging paradigm of Artificial Intelligence of Things (AIoT) is gaining significant attention. AIoT, which relies on large datasets and complex models, poses significant challenges to the limited resources of mobile systems. Many solutions have been proposed to address these challenges, including computation offloading to edge or cloud servers. In this article, we demonstrate that analyzing data access patterns and managing them efficiently are also crucial for further enhancing the performance of AIoT systems. Specifically, we propose an efficient write buffer management scheme tailored to mobile AIoT workloads. Our approach is based on an extensive analysis of data access patterns, revealing that the conventional buffer cache architectures and algorithms are inefficient for AIoT workloads due to access characteristics such as write dominance and long repetitive loops. To address these issues, we adopt a non-volatile write buffer at the storage layer and judiciously manage write loops. The key contributions of our study are as follows: 1) Despite relying solely on limited flush information at the storage layer, our scheme achieves performance comparable to full loop detection at the host system. 2) Our scheme requires no modifications to host OS semantics or buffer cache interfaces, ensuring high adaptability. 3) Experimental results demonstrate that our scheme significantly reduces storage traffic, outperforms representative algorithms such as LRU, LFU, LIRS, CLOCK-Pro, and LeCaR, and enhances data access efficiency for AIoT workloads.

In modern computer architectures, access latency varies considerably between different levels in the memory hierarchy. Consequently, applications with data access patterns that don't reuse much data in fast levels of the hierarchy incur additional delays. To improve the performance of complex, data-intensive applications, developers need tools that help them understand the causes of poor memory hierarchy utilization. While most performance tools associate metrics with functions or statements, in this paper we explore data-centric analyses that associate metrics not only with data accesses but also with data objects themselves. Our contributions are three-fold. First, we propose several refinements to existing data-centric techniques that enable accurate and low-overhead measurements. Second, we combine data-centric analysis with call path profiling; this combination of techniques relates inefficient access patterns back to data objects across complete dynamic call chains. Third, we developed a graphical user interface that gracefully presents our analysis results using a multiplicity of views, which helps users identify problematic accesses and data structures. We demonstrate the utility of our approach by showing how our tool identifies problematic data access patterns in several HPC applications and a pair of the SPEC CPU2006 benchmarks.

Software transactional memory (STM) has been proposed as a promising programming paradigm for shared memory multi-threaded programs as an alternative to conventional lock based synchronization primitives. Typical STM implementations employ a conflict detection scheme, which works with uniform access granularity, tracking shared data accesses either at word/cache line or at object level. It is well known that a single fixed access tracking granularity cannot meet the conflicting goals of reducing false conflicts without impacting concurrency adversely. A fine grained granularity while improving concurrency can have an adverse impact on performance due to lock aliasing, lock validation overheads, and additional cache pressure. On the other hand, a coarse grained granularity can impact performance due to reduced concurrency. Thus, in general, a fixed or uniform granularity access tracking (UGAT) scheme is application-unaware and rarely matches the access patterns of individual application or parts of an application, leading to sub-optimal performance for different parts of the application(s). In order to mitigate the disadvantages associated with UGAT scheme, we propose a Variable Granularity Access Tracking (VGAT) scheme in this paper. We propose a compiler based approach wherein the compiler uses inter-procedural whole program static analysis to select the access tracking granularity for different shared data structures of the application based on the application's data access pattern. We describe our prototype VGAT scheme, using TL2 as our STM implementation. Our experimental results reveal that VGAT-STM scheme can improve the application performance of STAMP benchmarks from 1.87% to up to 21.2%.

Data Grid environment is a geographically distributed that deal with date-intensive application in scientific and enterprise computing. In data-intensive applications data transfer is a primary cause of job execution delay. Data access time depends on bandwidth, especially when hierarchy of bandwidth appears in network. Effective job scheduling can reduce data transfer time by considering hierarchy of bandwidth and also dispatching a job to where the needed data are present. Additionally, replication of data from primary repositories to other locations can be an important optimization step to reduce the frequency of remote data access. Objective of dynamic replica strategies is reducing file access time which leads to reducing job runtime. In this paper we develop a job scheduling policy, called TLSS (Two level Scheduling strategy), and a dynamic data replication strategy, called TLRS (Two level Replication Strategy), to improve the data access efficiencies in a cluster grid. We study our approach and evaluate it through simulation. The results show that combination of TLSS and TLRS has improved 17% over other combinations.

High-performance computing (HPC) has enabled advancements in computation speed and resource cost by utilizing all available server resources and using parallelization for speedup. This computation scheme encourages simulation model development, massive data collection, and AI computation models, all which store and compute on massive amounts of data. Data compression has enhanced the performance of storing and transferring this HPC application data to enable acceleration, but the benefits of data compression can also be transferred to the active allocated memory used by the application. In-line compression is a compression method that keeps the application memory compressed in allocated memory, decompressing memory as its data is needed by the algorithm. The actively decompressed data size in allocated memory can be limited by grouping and compressing the data into blocks informed by the application's computational kernel's data access patterns and a selected compressor. This research explores factors such as block size and compressor choice on the runtime and memory usage of the Matrix Multiplication kernel. Matrix multiplication (MM) is a fundamental HPC algorithm that exemplifies the memory access patterns and use cases present in other HPC kernels. MM kernels provide a baseline for evaluating in-line compression due to MM's row-based data access patterns and the usage of several matrices to compute the resulting matrix. Trade-offs between memory size and the compression runtime necessitate tuned parameters for each in-line compression-enabled kernel. The results of this research demonstrate essential parameter trends, trade-offs, and the importance of locality in the kernel's data access patterns.

Data grids deal with a huge amount of data regularly. It is a fundamental challenge to ensure efficient accesses to such widely distributed data sets. Creating replicas to a suitable site by data replication strategy can increase the system performance. It shortens the data access time and reduces bandwidth consumption. In this paper, a dynamic data replication mechanism called Latest Access Largest Weight (LALW) is proposed. LALW selects a popular file for replication and calculates a suitable number of copies and grid sites for replication. By associating a different weight to each historical data access record, the importance of each record is differentiated. A more recent data access record has a larger weight. It indicates that the record is more pertinent to the current situation of data access. A Grid simulator, OptorSim, is used to evaluate the performance of this dynamic replication strategy. The simulation results show that LALW successfully increases the effective network usage. It means that the LALW replication strategy can find out a popular file and replicates it to a suitable site without increasing the network burden too much.

Large scientific collaborations often have multiple scientists accessing the same set of files while doing different analyses, which create repeated accesses to the large amounts of shared data located far away. These data accesses have long latency due to distance and occupy the limited bandwidth available over the wide-area network. To reduce the wide-area network traffic and the data access latency, regional data storage caches have been installed as a new networking service. To study the effectiveness of such a cache system in scientific applications, we examine the Southern California Petabyte Scale Cache for a high-energy physics experiment. By examining about 3TB of operational logs, we show that this cache removed 67.6% of file requests from the wide-area network and reduced the traffic volume on wide-area network by 12. 3TB (or 35.4%) an average day. The reduction in the traffic volume (35.4%) is less than the reduction in file counts (67.6%) because the larger files are less likely to be reused. Due to this difference in data access patterns, the cache system has implemented a policy to avoid evicting smaller files when processing larger files. We also build a machine learning model to study the predictability of the cache behavior. Tests show that this model is able to accurately predict the cache accesses, cache misses, and network throughput, making the model useful for future studies on resource provisioning and planning.

Data access latency is an important metric of system performance in data grid. By means of efficient replication strategy, the amount of data transferred in a wide area network will decrease, and the average access latency of data will decrease ultimately. The motivation of our research is to solve the optimized replica distribution problem in a data grid; that is, the system should utilize many replicas for every data with storage constraints to minimize the average access latency of data. This paper proposes a model of replication strategy in federated data grid and gives the optimized solution. The analysis results and simulation results show that the optimized replication strategy proposed in this paper is superior to LRU caching strategy, uniform replication strategy, proportional replication strategy and square root replication strategy in terms of wide area network bandwidth requirement and in the average access latency of data.

Data sharing increases our understanding of factors that influence health and diseases by enabling additional research questions from secondary data users, improving statistical power through combining multiple data sources, facilitating reproducibility and validation of research results, and supporting innovation with the development of research tools and methodologies. The NCI recently established an Office of Data Sharing (ODS), whose goal is to promote broad and equitable data sharing policies and processes to improve cancer knowledge and care. ODS advocates for open-access and broad data sharing policies to enable reproducibility, secondary use, knowledge sharing and innovation. ODS key priorities include: 1) coordinating the interpretation and implementation of the NCI and NIH Data-Sharing policies across the NCI, 2) streamlining data-access and -submission processes for NCI- and/or NIH-supported data repositories, 3) advocating for the proper balance of open-access, open-source, open-data-sharing policies while respecting the needs of research and participant communities. The NIH and NCI Data Sharing policies facilitate scientific progress by encouraging data access and sharing of genomic, phenotype, environmental, and behavioral data. Data access and sharing management, stewardship, and operating procedures are coordinated through a NIH governance structure of senior leadership and staff across NIH Institutes/Centers (ICs). NIH Data Access Committees (DACs) ensure data access based on shared principles and procedural standards of data management, security, privacy, and research participant protections. In efforts to improve data access efficiency, the ODS consolidated The Cancer Genome Atlas, intramural and extramural NCI DACs into a centralized operation, the NCI DAC. In comparison to other ICs, the NCI DAC receives the largest volume of data access requests (DARs) for access to controlled-tier level data. Specifically, the NCI DAC received 9214 DARs in 2017, representing 30% of all DARs received by NIH with a mean review time of 53 days, in comparison to the combined average of 35 days for all other NIH DACs. The NCI DAC oversees 216 studies registered in dbGaP, with 93 general research use (GRU) and 22 health, medical, biomedical (HMB) consent groups that allow data to be used for broader research uses. The centralized NCI DAC implemented data access review procedures for all DARs and expedited processes for projects requesting access to datasets with GRU and HMB data use limitations. These expedited procedures have produced a 26.5-fold decrease in time-to-decision from 53 to 2 days for researchers requesting access to NCI datasets. Additionally, ODS is committed to addressing health disparities ethics and is developing an Ethical, Economic, Legal, and Social Implications program that will address policies, practices, and ethical issues that arise from data sharing. Citation Format: Freddie L. Pruitt, Sylvia Shabaya Gayle, Nina Ghanem, Anna V. Mencarelli, Anthony R. Kerlavage, Vivian Ota Wang, Jaime M. Guidry Auvil. NCI’s Office of Data Sharing: Promoting broad & equitable policies and processes to improve cancer care [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3369.

Data grid integrates geographically distributed resources for solving data-sensitive scientific applications. As tasks are sensitive to data, dealing with large amount of data makes the requirement for efficiency in data access more critical. The goal of replica placement is to shorten data access time for enhancing the task execution performance. Therefore, replica placement strategies are often integral to task scheduling algorithms. However, all existing integration strategies make an assumption that the disk space of resources in data grid is unlimited. In this paper, we extended MinMin heuristic to cater to the situation where the disk space of a computational resource is limited. In addition, a heuristic replica placement algorithm is proposed, in which the limited disk space of a storage resource is considered as well. Another character of this heuristic replica placement algorithm is that it can map more than one hot file to several storage resources. We study our approach and evaluate it through simulation. The result shows that the integration of the two algorithms has improved the performance of data grid especially when the whole disk space of storage resources is relatively smaller than the amount of all data files.

As the cornerstone of the future intelligent transportation system, vehicular ad hoc networks (VANETs) have attracted intensive attention from the academic and industrial research communities in recent years. For widespread deployment of VANETs, security and privacy issues must be addressed properly. In this paper, we introduce the notion of pseudonym-based cryptography, and present a provable secure pseudonym-based cryptosystems with a trusted authority that includes a pseudonym-based multi-receiver encryption scheme, a pseudonym-based signature scheme, and a pseudonym-based key establishment protocol. We then propose a secure and efficient data access scheme for VANETs based on cooperative caching technology and our proposed pseudonym-based cryptosystems. On the one hand, the efficiency of data access are greatly improved by allowing the sharing and coordination of cached data among multiple vehicles. On the other hand, anonymity of the vehicles, data confidentiality, integrity and non-repudiation are guaranteed by employing our proposed pseudonym-based cryptosystems. Simulation results have shown that our proposed pseudonym-based cryptosystems are suitable to the VANETs environment.