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.

Abstract-Application's memory footprints are growing exponentially due to an increase in their data set and additional software layer. Modern RDMA capable networks such as InfiniBand and Myrinet with low latency and high bandwidth provide us a new vision to utilize remote memory. Remote idle memory can be exploited to improvement performance of memory intensive applications on individual nodes. Network swapping will be faster than traditional swapping to local disk. In this paper, we design a remote memory system for remote memory utilization in InfiniBand clusters. We present the architecture, communication method and algorithm of InfiniBand Block Device (IBD), which is implemented as loadable kernel module for version 3.5.0-45 of the Linux kernel. Especially, we discuss design issues transfer pages to remote memory. Our experiments show that IBD can bring more performance gain for applications whose working sets are larger than the local memory on a node but smaller than idle memory available on the cluster.Index Terms-Remote memory, distributed memory, swapping, cluster system, InfiniBand. I. INTRODUCTIONApplication's memory footprints are growing exponentially due to an increase in their data set and additional software layer. This memory requirement outpaces the growth in the capacity of current memory modules. Traditionally magnetic disks are used as the backing store for virtual memory. However the overall performance of applications is degraded due to the low speed of disks when applications need more memory than is physically available. In addition, since processor performance improves at a higher rate than disk seek latency, the cost of a disk access continues to increase with time [1].Modern networking technologies such as Infiniband and Myrinet with low latency of a few microseconds and high bandwidth of up to 10 Gbps provide us a new vision to utilize remote memory for local system performance improvement. It is clear that paging to idle memory is faster than paging to disk because network ram eliminates the physical seek time and the bandwidth of network connections is increasing faster than the bandwidth to disk .Several Remote memory mapping techniques deal with the remote memory as an extension to the local memory space. These techniques require inflexible malloc-like APIs and recompilation of the existing applications. Remote memory swapping techniques deal with the remote memory as a swap device. The aim of these techniques is partially to fill the performance gap between local memory and hard disk without modifying the OS or the running applications.Especially cluster systems, that encapsulate hundreds or even thousands of independent computing nodes within a single platform, have an imbalance of memory usage across different computing nodes. Therefore large amounts of idle cluster memory are almost always available for remote paging [4], [5]. Remote paging adds remote memory between main memory and disk in the local memory hierarchy. This caching technology provides an efficient way to boost...

Since human genome project finished, the cost for human genome analysis has decreased very rapidly. This results in the sharp increase of human genome data to be analyzed. As the need for fast analysis of very large bio data such as human genome increases, non IT researchers such as biologists should be able to execute fast and effectively many kinds of bio applications, which have a variety of characteristics, under HPC environment. To accomplish this purpose, a biologist need to define a sequence of bio applications as workflow easily because generally bio applications should be combined and executed in some order. This bio workflow should be executed in the form of distributed and parallel computing by allocating computing resources efficiently under HPC cluster system. Through this kind of job, we can expect better performance and fast response time of very large bio data analysis. This paper proposes a workflow-based data analysis system specialized for bio applications. Using this system, non-IT scientists and researchers can analyze very large bio data easily under HPC environment.