Metadata And Data Management In High Performance File And Storage Systems

With the advent of emerging e-Science applications, today\u27s scientific research increasingly relies on petascale-and-beyond computing over large data sets of the same magnitude. While the computational power of supercomputers has recently entered the era of petascale, the performance of their storage system is far lagged behind by many orders of magnitude. This places an imperative demand on revolutionizing their underlying I/O systems, on which the management of both metadata and data is deemed to have significant performance implications. Prefetching/caching and data locality awareness optimizations, as conventional and effective management techniques for metadata and data I/O performance enhancement, still play their crucial roles in current parallel and distributed file systems. In this study, we examine the limitations of existing prefetching/caching techniques and explore the untapped potentials of data locality optimization techniques in the new era of petascale computing. For metadata I/O access, we propose a novel weighted-graph-based prefetching technique, built on both direct and indirect successor relationship, to reap performance benefit from prefetching specifically for clustered metadata serversan arrangement envisioned necessary for petabyte scale distributed storage systems. For data I/O access, we design and implement Segment-structured On-disk data Grouping and Prefetching (SOGP), a combined prefetching and data placement technique to boost the local data read performance for parallel file systems, especially for those applications with partially overlapped access patterns. One high-performance local I/O software package in SOGP work for Parallel Virtual File System in the number of about 2000 C lines was released to Argonne National Laboratory in 2007 for potential integration into the production mode

Improving Mobile Video Streaming with Mobility Prediction and Prefetching in Integrated Cellular-WiFi Networks

We present and evaluate a procedure that utilizes mobility and throughput prediction to prefetch video streaming data in integrated cellular and WiFi networks. The effective integration of such heterogeneous wireless technologies will be significant for supporting high performance and energy efficient video streaming in ubiquitous networking environments. Our evaluation is based on trace-driven simulation considering empirical measurements and shows how various system parameters influence the performance, in terms of the number of paused video frames and the energy consumption; these parameters include the number of video streams, the mobile, WiFi, and ADSL backhaul throughput, and the number of WiFi hotspots. Also, we assess the procedure's robustness to time and throughput variability. Finally, we present our initial prototype that implements the proposed approach.Comment: 7 pages, 15 figure

HEC: Collaborative Research: SAM^2 Toolkit: Scalable and Adaptive Metadata Management for High-End Computing

The increasing demand for Exa-byte-scale storage capacity by high end computing applications requires a higher level of scalability and dependability than that provided by current file and storage systems. The proposal deals with file systems research for metadata management of scalable cluster-based parallel and distributed file storage systems in the HEC environment. It aims to develop a scalable and adaptive metadata management (SAM2) toolkit to extend features of and fully leverage the peak performance promised by state-of-the-art cluster-based parallel and distributed file storage systems used by the high performance computing community. There is a large body of research on data movement and management scaling, however, the need to scale up the attributes of cluster-based file systems and I/O, that is, metadata, has been underestimated. An understanding of the characteristics of metadata traffic, and an application of proper load-balancing, caching, prefetching and grouping mechanisms to perform metadata management correspondingly, will lead to a high scalability. It is anticipated that by appropriately plugging the scalable and adaptive metadata management components into the state-of-the-art cluster-based parallel and distributed file storage systems one could potentially increase the performance of applications and file systems, and help translate the promise and potential of high peak performance of such systems to real application performance improvements. The project involves the following components: 1. Develop multi-variable forecasting models to analyze and predict file metadata access patterns. 2. Develop scalable and adaptive file name mapping schemes using the duplicative Bloom filter array technique to enforce load balance and increase scalability 3. Develop decentralized, locality-aware metadata grouping schemes to facilitate the bulk metadata operations such as prefetching. 4. Develop an adaptive cache coherence protocol using a distributed shared object model for client-side and server-side metadata caching. 5. Prototype the SAM2 components into the state-of-the-art parallel virtual file system PVFS2 and a distributed storage data caching system, set up an experimental framework for a DOE CMS Tier 2 site at University of Nebraska-Lincoln and conduct benchmark, evaluation and validation studies

The Network Effects of Prefetching

Prefetching has been shown to be an effective technique for reducing user perceived latency in distributed systems. In this paper we show that even when prefetching adds no extra traffic to the network, it can have serious negative performance effects. Straightforward approaches to prefetching increase the burstiness of individual sources, leading to increased average queue sizes in network switches. However, we also show that applications can avoid the undesirable queueing effects of prefetching. In fact, we show that applications employing prefetching can significantly improve network performance, to a level much better than that obtained without any prefetching at all. This is because prefetching offers increased opportunities for traffic shaping that are not available in the absence of prefetching. Using a simple transport rate control mechanism, a prefetching application can modify its behavior from a distinctly ON/OFF entity to one whose data transfer rate changes less abruptly, while still delivering all data in advance of the user's actual requests

Adaptive runtime-assisted block prefetching on chip-multiprocessors

Memory stalls are a significant source of performance degradation in modern processors. Data prefetching is a widely adopted and well studied technique used to alleviate this problem. Prefetching can be performed by the hardware, or be initiated and controlled by software. Among software controlled prefetching we find a wide variety of schemes, including runtime-directed prefetching and more specifically runtime-directed block prefetching. This paper proposes a hybrid prefetching mechanism that integrates a software driven block prefetcher with existing hardware prefetching techniques. Our runtime-assisted software prefetcher brings large blocks of data on-chip with the support of a low cost hardware engine, and synergizes with existing hardware prefetchers that manage locality at a finer granularity. The runtime system that drives the prefetch engine dynamically selects which cache to prefetch to. Our evaluation on a set of scientific benchmarks obtains a maximum speed up of 32 and 10 % on average compared to a baseline with hardware prefetching only. As a result, we also achieve a reduction of up to 18 and 3 % on average in energy-to-solution.Peer ReviewedPostprint (author's final draft

Empowering a helper cluster through data-width aware instruction selection policies

Narrow values that can be represented by less number of bits than the full machine width occur very frequently in programs. On the other hand, clustering mechanisms enable cost- and performance-effective scaling of processor back-end features. Those attributes can be combined synergistically to design special clusters operating on narrow values (a.k.a. helper cluster), potentially providing performance benefits. We complement a 32-bit monolithic processor with a low-complexity 8-bit helper cluster. Then, in our main focus, we propose various ideas to select suitable instructions to execute in the data-width based clusters. We add data-width information as another instruction steering decision metric and introduce new data-width based selection algorithms which also consider dependency, inter-cluster communication and load imbalance. Utilizing those techniques, the performance of a wide range of workloads are substantially increased; helper cluster achieves an average speedup of 11% for a wide range of 412 apps. When focusing on integer applications, the speedup can be as high as 22% on averagePeer ReviewedPostprint (published version