Non-volatile memory (NVM) provides bulk storage capacity, like NAND flash, while providing low latency, like DRAM, at the same time. NVM enables high-performance, reliable, and cost-effective high performance systems by providing low-latency data access and high capacity storage compared to traditional disk-based system. As NVM becomes a novel tier in the memory hierarchy, efficiently utilizing NVM I/O capability is important. In this work, we evaluate the I/O performance of NVM in three aspects: the performance change with a varying number of concurrent accesses, the performance dif-ference between remote and local accesses, and the performance change with various access granularity. We also compare the performance of NVM file systems that handle the different I/O characteristics of NVM. Specifically, Odinfs is the state-of-the-art NVM file system that solves the performance degradation of NVM with large number of threads and remote NUMA node accesses. We further optimize Odinfs by solving the I/O performance degradation with a small number of threads. We evaluate the optimized version of Odinfs and show that the throughput of Odinfs is increased by 30.91 % with four or fewer threads.

In HPC systems, the increasing need for a higher level of concurrency has led to packing more cores within a single chip. However, since multiple processes share memory space, the frequent access to resources in critical sections where only atomic operation has to be executed can result in poor performance. In this paper, we focus on reducing lock contention on the memory management system of an HPC manycore architecture. One of the critical sections causing severe lock contention in the I/O path is in the page management system, which uses multiple Least Recently Used (LRU) lists with a single lock instance. To solve this problem, we propose a Finer-LRU scheme, which optimizes the page reclamation process by splitting LRU lists into multiple sub-lists, each having its own lock instance. Our evaluation result shows that the Finer-LRU scheme can improve sequential write throughput by 57.03% and reduce latency by 98.94% compared to the baseline Linux kernel version 5.2.8 in the Intel Knights Landing (KNL) architecture.

Genome sequence alignment is one of the basic procedure of genome sequencing analysis pipeline and also one of the most time-consuming parts. Including BigBWA, a number of tools were proposed to accelerate genome sequence alignment by parallelizing computation with Hadoop technologies. However, HDFS incurs considerable I/O overhead. In this research, we propose a new sequence alignment tool adopting Hadoop on Lustre. Based on BigBWA, we removed data transfer overhead caused by HDFS and parallelized whole I/O steps. Experimental result shows that our solution is five times faster than original BigBWA in a ten-node Lustre based Hadoop cluster.

Workflow technologies have become a major vehicle for the easy and efficient development of science applications. At the same time new computing environments such as the Cloud are now available. A challenge is to determine the right amount of resources to provision for an application. This paper introduces an algorithm named balanced time scheduling (BTS), which estimates the minimum number of virtual processors required to execute a workflow within a user-specified finish time. The resource estimate of BTS is abstract, so it can be easily integrated with any resource description language or any resource provisioning system. The experimental results with a number of synthetic workflows demonstrate that BTS can estimate the computing capacity close to the optimal. The algorithm is scalable so that its turnaround time is only tens of seconds even with workflows having thousands of tasks and edges.

Although WSRF (Web Services Resource Framework) and Java-based hosting environment have been successful in dealing with the heterogeneity of resources and the diversity of applications, the current Grid middleware has several limitations to support on-demand resource provisioning effectively.

Recently grid computing has started to leverage Web services technology by proposing OGSI-standard. OGSI standard defines the grid service, which presents unified interfaces to every participant of grid. In current Glubus Toolkit3(GT3),which is an implementation of OGSI, grid service factories should be deployed manually into resources to provide grid services. However, it is necessary to dynamically allocate proper amount of resource, since the demand for resource of service provider changes over time. In this paper, we propose a architecture to enable on-demand resource provisioning. We develop universal factory service (UFS) that provides a dynamic grid service deployment mechanism and a resource broker called door service. Through the experiments, we show that grid services can adaptively exploit resources according to the request rates.