As the development of marine economy and population explosion, coastal areas is suffering great pressure - because of the immigration from inland to the developed cities along east China. Island coastal zones, which is a specific ecosystem surrounded by the sea, is more sensitive to human activities, e.g. reclamations. It is essential to monitor the dynamic changes of the island coastal areas to retrieve the siltation pattern of the surrounding open-sea and their impacts to island coastlines using remote sensing technique. In this paper, a time-series monitoring using Landsat images is performed to monitor the changes of a sand dam in the island coast zone, aiming at analyzing the effects of human exploitation. The results show great potential of using remote sensing images for coast zone dynamic monitoring.

BLAST, short for Basic Local Alignment Search Tool, is a fundamental algorithm in the life sciences that compares biological sequences. However, with the advent of next-generation sequencing (NGS) and increase in sequence read-lengths, whether at the outset or downstream from NGS, the exponential growth of sequence databases is arguably outstripping our ability to analyze the data. Though several recent studies have utilized the graphics processing unit (GPU) to speedup the BLAST algorithm for searching protein sequences (i.e., BLASTP), these studies used coarse-grained parallel approaches, where one sequence alignment is mapped to only one thread. Moreover, due to the irregular memory access patterns in BLASTP, there remain significant challenges to map the most time-consuming phases (i.e., hit detection and ungapped extension) to the GPU using a fine-grained multithreaded approach. To address the above issues, we propose cuBLASTP, an efficient fine-grained BLASTP implementation for the GPU using CUDA. Our cuBLASTP realization encompasses many research contributions, including (1) memory-access reordering to reorder hits from column-major order to diagonal-major order, (2) position-based indexing to map a hit with a packed data structure to a bin, (3) aggressive hit filtering to eliminate hits beyond the threshold distance along the diagonal, (4) diagonal-based parallelism and hit-based parallelism for ungapped extension to extend sequences with different lengths in databases, and (5) hierarchical buffering to reduce memory-access overhead for the core data structures. The experimental results show that on a NVIDIA Kepler GPU, cuBLASTP delivers up to a 5.0-fold speedup over sequential FSA-BLAST and a 3.7-fold speedup over multithreaded NCBI-BLAST for the overall program execution. In addition, compared with GPU-BLASTP (the fastest GPU implementation of BLASTP to date), cuBLASTP achieves up to a 2.8-fold speedup for the kernel execution on the GPU and a 1.8-fold speedup for the overall program execution.

While Graphics Processing Units (GPU) have gained much success in general purpose computing in recent years, their programming is still difficult, due to, particularly, explicitly managed GPU memory and manual CPU-GPU data transfer. Despite recent calls for managing GPU resources as first-class citizens in the operating system, a mature GPU memory management mechanism is still missing, which leads to reinventing the wheels in various GPU system software. Meanwhile, due to ever enlarging problem sizes, we urgently need a system-level mechanism for unified CPU-GPU memory management. In this work, we present the design of Region-based Software Virtual Memory (RSVM), a software virtual memory running on both CPU and GPU in a distributed and cooperative way. In addition to automatic GPU memory management and GPU-CPU data transfer, RSVM offers two novel features: 1) GPU kernel-issued on-demand data fetching from the host into the GPU memory, and 2) intra-kernel transparent GPU memory swapping into the main memory. Our study reveals important insights on the challenges and opportunities of building unified virtual memory systems for heterogeneous computing. Experimental results on real GPU benchmarks demonstrate that, though it incurs a small overhead, RSVM can transparently scale GPU kernels to large problem sizes exceeding the device memory size limit; developers write the same code for different problem sizes, but still can optimize on data layout definition accordingly. Our evaluation also identifies missing GPU architecture features for better system software efficiency.

Computational biology sequence alignment tools using the Burrows-Wheeler Transform (BWT) are widely used in next-generation sequencing (NGS) analysis. However, despite extensive optimization efforts, the performance of these tools still cannot keep up with the explosive growth of sequencing data. Through an in-depth performance analysis of BWA, a popular BWT-based aligner on multicore architectures, we demonstrate that such tools are limited by memory bandwidth due to their irregular memory access patterns. We then propose a locality-aware implementation of BWA that aims at optimizing its performance by better exploiting the caching mechanisms of modern multicore processors. Experimental results show that our improved BWA implementation can reduce last-level cache (LLC) misses by 30% and translation look aside buffer (TLB) misses by 20%, resulting in up to 2.6-fold speedup over the original BWA implementation.

This paper presents a framework to support transparent, live migration of virtual GPU accelerators in a virtualized execution environment. Migration is a critical capability in such environments because it provides support for fault tolerance, on-demand system maintenance, resource management, and load balancing in the mapping of virtual to physical GPUs. Techniques to increase responsiveness and reduce migration overhead are explored. The system is evaluated by using four application kernels and is demonstrated to provide low migration overheads. Through transparent load balancing, our system provides a speedup of 1.7 to 1.9 for three of the four application kernels.

In the past, evaluating the architectural innovation of parallel computing devices relied on a benchmark suite based on existing programs, e.g., EEMBC or SPEC. However, with the growing ubiquity of parallel computing devices, we argue that it is unclear how best to express parallel computation, and hence, a need exists to identify a higher level of abstraction for reasoning about parallel application requirements. Therefore, the goal of this combination "Work-in-Progress and Vision" paper is to delineate application requirements in a manner that is not overly specific to individual applications or the optimizations used for certain hardware platforms, so that we can draw broader conclusions about hardware requirements. Our initial effort, dubbed "OpenCL and the 13 Dwarfs" or OCD for short, realizes Berkeley's 13 computational dwarfs of scientific computing in OpenCL, where each dwarf captures a pattern of computation and communication that is common to a class of important applications.

Atomic operations are important building blocks in supporting general-purpose computing on graphics processing units (GPUs). For instance, they can be used to coordinate execution between concurrent threads, and in turn, assist in constructing complex data structures such as hash tables or implementing GPU-wide barrier synchronization. While the performance of atomic operations has improved substantially on the latest NVIDIA Fermi-based GPUs, system-provided atomic operations still incur significant performance penalties on AMD GPUs. A memory-bound kernel on an AMD GPU, for example, can suffer severe performance degradation when including an atomic operation, even if the atomic operation is never executed. In this paper, we first quantify the performance impact of atomic instructions to application kernels on AMD GPUs. We then propose a novel software-based implementation of atomic operations that can significantly improve the overall kernel performance. We evaluate its performance against the system-provided atomic using two micro-benchmarks and four real applications. The results show that using our software based atomic operations on an AMD GPU can speedup an application kernel by 67-fold over the same application kernel but with the (default) system-provided atomic operations.

MapReduce is a programming model from Google that facilitates parallel processing on a cluster of thousands of commodity computers. The success of MapReduce in cluster environments has motivated several studies of implementing MapReduce on a graphics processing unit (GPU), but generally focusing on the NVIDIA GPU. Our investigation reveals that the design and mapping of the MapReduce framework needs to be revisited for AMD GPUs due to their notable architectural differences from NVIDIA GPUs. For instance, current state-of-the-art MapReduce implementations employ atomic operations to coordinate the execution of different threads. However, atomic operations can implicitly cause inefficient memory access, and in turn, severely impact performance. In this paper, we propose Streamer, an OpenCL MapReduce framework optimized for AMD GPUs. With efficient atomic-free algorithms for output handling and intermediate result shuffling, Stream MR is superior to atomic-based MapReduce designs and can outperform existing atomic-free MapReduce implementations by nearly five-fold on an AMD Radeon HD 5870.

Dynamic programming (DP) is an important computational method for solving a wide variety of discrete optimization problems such as scheduling, string editing, packaging, and inventory management. In general, DP is classified into four categories based on the characteristics of the optimization equation. Because applications that are classified in the same category of DP have similar program behavior, the research community has sought to propose general solutions for parallelizing each category of DP. However, most existing studies focus on running DP on CPU-based parallel systems rather than on accelerating DP algorithms on the graphics processing unit (GPU). This paper presents the GPU acceleration of an important category of DP problems called nonserial polyadic dynamic programming (NPDP). In NPDP applications, the degree of parallelism varies significantly in different stages of computation, making it difficult to fully utilize the compute power of hundreds of processing cores in a GPU. To address this challenge, we propose a methodology that can adaptively adjust the thread-level parallelism in mapping a NPDP problem onto the GPU, thus providing sufficient and steady degrees of parallelism across different compute stages. We realize our approach in a real-world NPDP application -- the optimal matrix parenthesization problem. Experimental results demonstrate our method can achieve a speedup of 13.40 over the previously published GPU algorithm.

The "Basic Local Alignment Search Tool'' (BLAST) is arguably the most widely used computational tool in bioinformatics. However, the computational power required for routine BLAST analysis has been outstripping Moore's Law due to the exponential growth in the size of the genomic sequence databases that BLAST searches on. To address the above issue, we propose the design and optimization of the BLAST algorithm for searching protein sequences (i.e., BLASTP) in a heterogeneous computing system. The end result is a BLASTP implementation that delivers a seven-fold speedup over the sequential BLASTP for the most computationally intensive phase (i.e., hit detection and ungapped extension) on a NVIDIA Fermi C2050 GPU. In addition, when pipelining the processing on a dual-core CPU and the NVIDIA Fermi GPU, our implementation can achieve a six-fold speedup for the overall program execution.

With the explosive growth of genomic information, the searching of sequence databases has emerged as one of the most computation and data-intensive scientific applications. Our previous studies suggested that parallel genomic sequence-search possesses highly irregular computation and I/O patterns. Effectively addressing these runtime irregularities is thus the key to designing scalable sequence-search tools on massively parallel computers. While the computation scheduling for irregular scientific applications and the optimization of noncontiguous file accesses have been well-studied independently, little attention has been paid to the interplay between the two. In this paper, we systematically investigate the computation and I/O scheduling for data-intensive, irregular scientific applications within the context of genomic sequence search. Our study reveals that the lack of coordination between computation scheduling and I/O optimization could result in severe performance issues. We then propose an integrated scheduling approach that effectively improves sequence-search throughput by gracefully coordinating the dynamic load balancing of computation and high-performance noncontiguous I/O.

MapReduce offers an ease-of-use programming paradigm for processing large data sets, making it an attractive model for distributed volunteer computing systems. However, unlike on dedicated resources, where MapReduce has mostly been deployed, such volunteer computing systems have significantly higher rates of node unavailability. Furthermore, nodes are not fully controlled by the MapReduce framework. Consequently, we found the data and task replication scheme adopted by existing MapReduce implementations woefully inadequate for resources with high unavailability.

The Map Reduce programming model simplifies large-scale data processing on commodity clusters by having users specify a map function that processes input key/value pairs to generate intermediate key/value pairs, and a reduce function that merges and converts intermediate key/value pairs into final results. Typical Map Reduce implementations such as Hadoop enforce barrier synchronization between the map and reduce phases, i.e., the reduce phase does not start until all map tasks are finished. In turn, this synchronization requirement can cause inefficient utilization of computing resources and can adversely impact performance. Thus, we present and evaluate two different approaches to cope with the synchronization drawback of existing Map Reduce implementations. The first approach, hierarchical reduction, starts a reduce task as soon as a predefined number of map tasks completes, it then aggregates the results of different reduce tasks following a tree structure. The second approach, incremental reduction, starts a predefined number of reduce tasks from the beginning and has each reduce task incrementally reduce records collected from map tasks. Together with our performance modeling, we evaluate different reducing approaches with two real applications on a 32-node cluster. The experimental results have shown that incremental reduction outperforms hierarchical reduction in general. Also, incremental reduction can speed-up the original Hadoop implementation by up to 35.33% for the word count application and 57.98% for the grep application. In addition, incremental reduction outperforms the original Hadoop in an emulated cloud environment with heterogeneous compute nodes.

Abstract Achieving high performance for distributed I/O on a wide‐area network continues to be an elusive holy grail. Despite enhancements in network hardware as well as software stacks, achieving high‐performance remains a challenge. In this paper, our worldwide team took a completely new and non‐traditional approach to distributed I/O, calledParaMEDIC: Parallel Metadata Environment for Distributed I/O and Computing, by utilizing application‐specifictransformationof data to orders of magnitude smaller metadata before performing the actual I/O. Specifically, this paper details our experiences in deploying a large‐scale system to facilitate the discovery of missing genes and constructing a genome similarity tree by encapsulating the mpiBLAST sequence‐search algorithm into ParaMEDIC. The overall project involved nine computational sites spread across the U.S. and generated more than a petabyte of data that was ‘teleported’ to a large‐scale facility in Tokyo for storage. Copyright © 2010 John Wiley & Sons, Ltd.

Many large-scale applications simultaneously rely on multiple resources for efficient execution. For example, such applications may require both large compute and storage resources; however, very few supercomputing centers can provide large quantities of both. Thus, data generated at the compute site oftentimes has to be moved to a remote storage site for either storage or visualization and analysis. Clearly, this is not an efficient model, especially when the two sites are distributed over a wide-area network.

BLAST is a widely used software toolkit for genomic sequence search. mpiBLAST is a freely available, open-source parallelization of BLAST that uses database segmentation to allow different worker processes to search (in parallel) unique segments of the database. After searching, the workers write their output to a filesystem. While mpiBLAST has been shown to achieve high performance in clusters with fast local filesystems, its I/O processing remains a concern for scalability, especially in systems having limited I/O capabilities such as distributed filesystems spread across a wide-area network. Thus, we present ParaMEDIC---a novel environment that uses application-specific semantic information to compress I/O data and improve performance in distributed environments. Specifically, for mpiBLAST, ParaMEDIC partitions worker processes into compute and I/O workers. Compute workers, instead of directly writing the output to the filesystem, the workers process the output using semantic knowledge about the application to generate metadata and write the metadata to the filesystem. I/O workers, which physically reside closer to the actual storage, then process this metadata to re-create the actual output and write it to the filesystem. This approach allows ParaMEDIC to reduce I/O time, thus accelerating mpiBLAST by as much as 25-fold.

This paper presents our first experiences in mapping and optimizing genomic sequence search onto the massively parallel IBM Blue Gene/P (BG/P) platform. Specifically, we performed our work on mpiBLAST, a parallel sequence-search code that has been optimized on numerous supercomputing environments. In doing so, we identify several critical performance issues. Consequently, we propose and study different approaches for mapping sequence-search and parallel I/O tasks on such massively parallel architectures.We demonstrate that our optimizations can deliver nearly linear scaling (93% efficiency) on up to 32,768 cores of BG/P. In addition, we show that such scalability enables us to complete a large-scale bioinformatics problem - sequence searching a microbial genome database against itself to support the discovery of missing genes in genomes - in only a few hours on BG/P. Previously, this problem was viewed as computationally intractable in practice.

Parallel sequence-search tools are rising in popularity among computational biologists. With the rapid growth of sequence databases, database segmentation is the trend of the future for such search tools. While I/O currently is not a significant bottleneck for parallel sequence-search tools, future technologies including faster processors, customized computational hardware such as FPGAs, improved search algorithms, and exponentially growing databases emphasize an increasing need for efficient parallel I/O in future parallel sequence-search tools. Our paper focuses on examining different I/O strategies for these future tools in a modern parallel file system (PVFS2). Because implementing and comparing various I/O algorithms in every search tool is labor-intensive and time-consuming, we introduce S3aSim, a general simulation framework for sequence-search which allows us to quickly implement, test, and profile various I/O strategies. We examine a variety of I/O strategies (e.g., master-writing and various worker-writing strategies using individual and collective I/O methods) for storing result data in sequence-search tools such as mpiBLAST, pioBLAST, and parallel HMMer. Our experiments fully detail the interaction of computing and I/O within a full application simulation as opposed to typical I/O-only benchmarks

The Basic Local Alignment Search Tool (BLAST) allows bioinformaticists to characterize an unknown sequence by comparing it against a database of known sequences. The similarity between sequences enables biologists to detect evolutionary relationships and infer biological properties of the unknown sequence.mpiBLAST, our parallel BLAST, decreases the search time of a 300 KB query on the current NT database from over two full days to under 10 minutes on a 128-processor cluster and allows larger query files to be compared. Consequently, we propose to compare the largest query available, the entire NT database, against the largest database available, the entire NT database. The result of this comparison will provide critical information to the biology community, including insightful evolutionary, structural, and functional relationships between every sequence and family in the NT database.Preliminary projections indicated that to complete the above task in a reasonable length of time required more processors than were available to us at a single site. Hence, we assembled GreenGene, an ad-hoc grid that was constructed "on the fly" from donated computational, network, and storage resources during last year's SC|05. GreenGene consisted of 3048 processors from machines that were distributed across the United States. This paper presents a case study of mpiBLAST on GreenGene --- specifically, a pre-run characterization of the computation, the hardware and software architectural design, experimental results, and future directions.