LESSA: a Library of Efficient, Scalable and Service-oriented Algorithms


1. Large-Scale Biological Sequence Analysis System

As part of my LESSA library, this project has been the core of my research on parallel and distributed algorithm design for bioinformatics, by employing a variety of tightly-coupled and loosely-coupled computing architectures, including heterogeneous computers with accelerators (e.g. Intel SSE, Intel AVX, Intel Xeon Phis, NVIDIA GPUs and AMD GPUs), cluster computing and cloud computing. My final objective is to establish an analysis system for large-scale biological sequences, in order to solve some critical and bottleneck problems in bioinformatics and computational biology, such as genome sequencing based on high-throughput sequencing technologies, meta-genomics, motif discovery, sequence alignment, and phylogenetic inference. Fig. 1 illustrates the diagram of an imaginaory biological data analysis system.

Fig. 1 System diagram for large-scale biological sequence analysis

Example work:

(The full lists of my software and publications are available here)

2. Parallel Full-Text Search and Pattern Search

As part of my LESSA library, this project aims to design parallel and memory-efficient algorithms for full-text indexing and pattern search. In particular, I will investigate the parallel and space-efficient construction of four popular full-text indexing data structures: Burrows-Wheeler transform, FM-index, suffix array and enhanced suffix array, on heterogeneous computing architectures comprising multi-core CPUs and accelerators (specifically, NVIDIA and AMD GPUs and Intel Xeon Phi coprocessors). Based on these indexing data structures, I will further explore full-text pattern search, e.g. searching for maximal exact matches, super maximal exact matches, and approximate pattern matches, which is fudamental and critical to a myriad of applications in various fields such as Bioinformatics (e.g. next-generation sequencing read alignment and genome assembly) and text/data mining.

Both full-text indexing and full-text pattern search engines will be ultimately offered in the form of a library. The libray will provide a unified high-level programming interface for different types of the underlying processing units, which will enhance the productivity of developers and enables performance portability between multi-core CPUs and accelerators. Furthermore, on a heterogeneous computer with accelerators, this library intends to have the capability of autonomously selecting the "most efficient" parallelization, which may run on any of the different types of processing units or even on some hybrid combinations, at runtime. In this fashion, while deploying the parallel algorithms from the library, developers do not need to care more about the details of the underlying hardware configuration, and thereby can pay more attention to the development of other parts of a program. Fig. 2 shows the high-level system diagram for my parallel full-text indexing and pattern search project.

Fig. 2 System diagram for parallel full-text indexing and pattern search

Example work:

Full-text Indexing

Full-text Pattern Search

3.Compute Unified Parallel Building Blocks (CUPBB)

This project aims to build a Compute Unified Parallel Building Blocks (CUPBB) template library using heterogeneous computing. This library will contain popular fundamental building blocks for scientific computing and data science, including, but not limited to, sparse linear algebra (e.g. SpMV), scan, reduction, sort, k-nearest neighbors and etc. For example, sparse linear algebra is recognized as one of the seven dwarfs By UC Berkeley, for parallel computing research, and is believed to be of high importance to science and engineering in the coming years. As an essential primitive in sparse linear algebra, sparse matrix-vector multiplication (SpMV) has garnered intense scientific attention and has been used in a variety of scientific computing applications such as bioinformatics, data mining, graph analytics and iterative methods in scientific computing. I have done some work on parallel sparse linear algebra on CUDA-enabled GPUs and parallel pairwise correlation computation on Intel Xeon Phi clusters for data science (e.g. for feature selection in machine learning)
Fig. 3 (a) speedups of LightSpMV over CUSP on a Telsa K40c GPU and (b) speedups over cuSPARASE on a Tesla K40c GPU

Example work:

4. Compact Computing for Big Data

As data volume increases exponentially and data compression is popularly used in data centers, I would expect that directly operating on compressed data would become commonplace in the future. However, parallel processing of compressed data is a challenging proposition for both shared-memory and distributed-memory systems and the challenges could come from constrained random accesses to uncompressed content, independent decompression of data blocks, balanced distribution of data, memory and computation, adaption of existing algorithms and applications to meet the requirements of compressed data processing and so on. Based on these concerns, I am conceiving of a new concept of computing and name it as Compact Computing tentatively. In general, Compact Computing targets robust, flexible and reproducible parallel processing of big data and consists of three core components, in principle: (1) tightly-coupled architectures, (2) compressive and elastic data representation, and (3) efficient, scalable and service-oriented algorithms and applications. In this context, component 1 can comprise conventional CPUs, a diversity of accelerators (e.g. FPGAs, GPUs, MIC processors and etc.) and fast interconnect communication facilities; component 2 concentrates on data structures and formats that enable efficient on-the-fly streaming compression and decompression; and component 3 targets the development of algorithms and applications that enable robust and efficient processing of big data streams in parallel. By centering around data, Compact Computing enables full-stack computation by tightly coupling algorithms with systems.

Example work: