Computing Platforms for Big Biological Data Analytics: Perspectives and Challenges.

The last decade has witnessed an explosion in the amount of available biological sequence data, due to the rapid progress of high-throughput sequencing projects. However, the biological data amount is becoming so great that traditional data analysis platforms and methods can no longer meet the need to rapidly perform data analysis tasks in life sciences. As a result, both biologists and computer scientists are facing the challenge of gaining a profound insight into the deepest biological functions from big biological data. This in turn requires massive computational resources. Therefore, high performance computing (HPC) platforms are highly needed as well as efficient and scalable algorithms that can take advantage of these platforms. In this paper, we survey the state-of-the-art HPC platforms for big biological data analytics. We first list the characteristics of big biological data and popular computing platforms. Then we provide a taxonomy of different biological data analysis applications and a survey of the way they have been mapped onto various computing platforms. After that, we present a case study to compare the efficiency of different computing platforms for handling the classical biological sequence alignment problem. At last we discuss the open issues in big biological data analytics

Exploiting Graphics Processing Units for Massively Parallel Multi-Dimensional Indexing

Department of Computer EngineeringScientific applications process truly large amounts of multi-dimensional datasets. To efficiently navigate such datasets, various multi-dimensional indexing structures, such as the R-tree, have been extensively studied for the past couple of decades. Since the GPU has emerged as a new cost-effective performance accelerator, now it is common to leverage the massive parallelism of the GPU in various applications such as medical image processing, computational chemistry, and particle physics. However, hierarchical multi-dimensional indexing structures are inherently not well suited for parallel processing because their irregular memory access patterns make it difficult to exploit massive parallelism. Moreover, recursive tree traversal often fails due to the small run-time stack and cache memory in the GPU. First, we propose Massively Parallel Three-phase Scanning (MPTS) R-tree traversal algorithm to avoid the irregular memory access patterns and recursive tree traversal so that the GPU can access tree nodes in a sequential manner. The experimental study shows that MPTS R-tree traversal algorithm consistently outperforms traditional recursive R-Tree search algorithm for multi-dimensional range query processing. Next, we focus on reducing the query response time and extending n-ary multi-dimensional indexing structures - R-tree, so that a large number of GPU threads cooperate to process a single query in parallel. Because the number of submitted concurrent queries in scientific data analysis applications is relatively smaller than that of enterprise database systems and ray tracing in computer graphics. Hence, we propose a novel variant of R-trees Massively Parallel Hilbert R-Tree (MPHR-Tree), which is designed for a novel parallel tree traversal algorithm Massively Parallel Restart Scanning (MPRS). The MPRS algorithm traverses the MPHR-Tree in mostly contiguous memory access patterns without recursion, which offers more chances to optimize the parallel SIMD algorithm. Our extensive experimental results show that the MPRS algorithm outperforms the other stackless tree traversal algorithms, which are designed for efficient ray tracing in computer graphics community. Furthermore, we develop query co-processing scheme that makes use of both the CPU and GPU. In this approach, we store the internal and leaf nodes of upper tree in CPU host memory and GPU device memory, respectively. We let the CPU traverse internal nodes because the conditional branches in hierarchical tree structures often cause a serious warp divergence problem in the GPU. For leaf nodes, the GPU scans a large number of leaf nodes in parallel based on the selection ratio of a given range query. It is well known that the GPU is superior to the CPU for parallel scanning. The experimental results show that our proposed multi-dimensional range query co-processing scheme improves the query response time by up to 12x and query throughput by up to 4x compared to the state-of-the-art GPU tree traversal algorithm.ope

Exploring Functional Acceleration of OpenCL on FPGAs and GPUs Through Platform-Independent Optimizations

OpenCL has been proposed as a means of accelerating functional computation using FPGA and GPU accelerators. Although it provides ease of programmability and code portability, questions remain about the performance portability and underlying vendor's compiler capabilities to generate efficient implementations without user-dened, platform specic optimizations. In this work, we systematically evaluate this by formalizing a design space exploration strategy using platform-independent micro-architectural and application-specic optimizations only. The optimizations are then applied across Altera FPGA, NVIDIA GPU and ARM Mali GPU platforms for three computing examples, namely matrix-matrix multiplication, binomial-tree option pricing and 3-dimensional nite difference time domain. Our strategy enables a fair comparison across platforms in terms of throughput and energy efficiency by using the same design effort. Our results indicate that FPGA provides better performance portability in terms of achieved percentage of device's peak performance (68%) compared to NVIDIA GPU (20%) and also achieves better energy efficiency (up to 1:4X) for some of the considered cases without requiring in-depth hardware design expertise

The Parallelism Motifs of Genomic Data Analysis

Genomic data sets are growing dramatically as the cost of sequencing continues to decline and small sequencing devices become available. Enormous community databases store and share this data with the research community, but some of these genomic data analysis problems require large scale computational platforms to meet both the memory and computational requirements. These applications differ from scientific simulations that dominate the workload on high end parallel systems today and place different requirements on programming support, software libraries, and parallel architectural design. For example, they involve irregular communication patterns such as asynchronous updates to shared data structures. We consider several problems in high performance genomics analysis, including alignment, profiling, clustering, and assembly for both single genomes and metagenomes. We identify some of the common computational patterns or motifs that help inform parallelization strategies and compare our motifs to some of the established lists, arguing that at least two key patterns, sorting and hashing, are missing