Design of MAHA Supercomputing System for Human Genome Analysis

Abstract

During the past decade, many changes and attempts have been tried and are continued developing new technologies in the computing area. The brick wall in computing area, especially power wall, changes computing paradigm from computing hardwares including processor and system architecture to programming environment and application usage. The high performance computing (HPC) area, especially, has been experienced catastrophic changes, and it is now considered as a key to the national competitiveness. In the late 2000's, many leading countries rushed to develop Exascale supercomputing systems, and as a results tens of PetaFLOPS system are prevalent now. In Korea, ICT is well developed and Korea is considered as a one of leading countries in the world, but not for supercomputing area. In this paper, we describe architecture design of MAHA supercomputing system which is aimed to develop 300 TeraFLOPS system for bio-informatics applications like human genome analysis and protein-protein docking. MAHA supercomputing system is consists of four major partscomputing hardware, file system, system software and bio-applications. MAHA supercomputing system is designed to utilize heterogeneous computing accelerators (co-processors like GPGPUs and MICs) to get more performance/$, performance/area, and performance/power. To provide high speed data movement and large capacity, MAHA file system is designed to have asymmetric cluster architecture, and consists of metadata server, data server, and client file system on top of SSD and MAID storage servers. MAHA system softwares are designed to provide user-friendliness and easy-to-use based on integrated system management component -like Bio Workflow management, Integrated Cluster management and Heterogeneous Resource management. MAHA supercomputing system was first installed in Dec., 2011. The theoretical performance of MAHA system was 50 TeraFLOPS and measured performance of 30.3 TeraFLOPS with 32 computing nodes. MAHA system will be upgraded to have 100 TeraFLOPS performance at Jan., 2013.

Apache Airavata as a Laboratory

“…Going beyond the mechanics of interacting with individual resources, gateways need the ability to optimize research usage across all the resources they use. For example, gateways such as BioVLab [17] enable a wide range of workflows, each specific to a research problem, to be deployed both on computational clouds (Amazon EC2) and Grid Resources (MAHA Supercomputing System, Korea [13]). Users choose EC2 as computational alternative for on-demand computing.…”

MAP task allocation strategy in an ARM-based Hadoop cluster by using local storage as split cache

“…To prevent semiconductor elements from being damaged due to the excessive heat, additional cooling systems to decrease the temperature are necessary and inevitably require more power consumption. The excessive power consumption of processors and cooling systems is the hindrances to increase the performance and throughput of the system, which is pointed out by Kim et al (2013), Kogge et al (2008) and Kim and Kim (2010).…”

“…In the early 2000s, semiconductor technology makes manufacturer enabling to implement 100 nm-or 20 ns-scale semiconductor as described by Kim et al (2013). High integration and high-speed operation of semiconductors, however, have caused an increase of power consumption and excessive heat generation.…”

“…Weijun et al (2009) pointed out that GPGPU can use the processing power of GPU, which was originally developed for graphics processing, to process the general tasks. And Kim et al (2012Kim et al ( , 2013) also pointed out that many recent types of research focus on designing supercomputers by means of GPGPU. NVIDIA Corp. develops and produces a GPGPU of CUDA structure.…”