Accelerating BLAST Computation on an FPGA-enhanced PC Cluster

This paper introduces an FPGA-based scheme to accelerate mpiBLAST, which is a parallel sequence alignment algorithm for computational biology. Recent rapidly growing biological databases for sequence alignment require highthroughput storage and network rather than computing speed. Our scheme utilizes a specialized hardware configured on an FPGA-board which connects flash storage and other FPGAboards directly. The specialized hardware configured on the FPGAs, we call a Data Stream Processing Engine (DSPE), take a role for preprocessing to adjust data for high-performance multi- and many- core processors simultaneously with offloading system-calls for storage access and networking. DSPE along the datapath achieves in-datapath computing which applies operations for data streams passing through the FPGA. Two functions in mpiBLAST are implemented using DSPE to offload operations along the datapath. The first function is database partitioning, which distributes the biological database to multiple computing nodes before commencing the BLAST processes. Using DSPE, we observe a 20-fold improvement in computation time for the database partitioning operation. The second function is an early part of the BLAST process that determines the positions of sequences for more detailed computations. We implement IDP-BLAST (In-datapath BLAST), which annotates positions in data streams from solid-state drives. We show that IDP-BLAST accelerates the computation time of the preprocess of BLAST by a factor of three hundred by offloading heavy operations to the introduced special hardware

Sharing Computing Resources with Virtual Machines by Transparent Data Access

Cloud computing has rapid growth in enterprise and academic areas. Computing platform makes up the transition from physical servers to virtual machines (VMs) in the cloud. Instead of many advantages, VMs remain several problems to employ effective utilization of physical computing resources, especially many-core accelerators. Even though GPGPU is a hopeful solution for high-load applications, existing methods to utilize GPUs from VMs are subjected to various restraints. In order to solve this problem, we propose a flexible method to share external computing resources by providing transparent access for data in the VMs. By committing commands to a computing host which processes the jobs as substitution, VMs can process high load jobs as necessary even if the VM has a tiny configuration. The computing host mounts the working directories in the VMs and enqueues jobs committed by the VMs. Experimental results show that the overhead of our implementation is sufficiently small in the low I/O load processes

A Light-weight Content Distribution Scheme for Cooperative Caching in Telco-CDNs

A key technique to reduce the rapid growing of video-on-demand’s traffic is a cooperative caching strategy aggregating multiple cache storages. Many internet service providers have considered the use of cache servers on their networks as a solution to reduce the traffic. Existing schemes often periodically calculate a sub-optimal allocation of the content caches in the network. However, such approaches require a large computational overhead that cannot be amortized in a presence of frequent changes of the contents’ popularities. This paper proposes a light-weight scheme for a cooperative caching that obtains a sub-optimal distribution of the contents by focusing on their popularities. This was made possible by adding color tags to both cache servers and contents. In addition, we propose a hybrid caching strategy based on Least Frequently Used (LFU) and Least Recently Used (LRU) schemes, which efficiently manages the contents even with a frequent change in the popularity. Evaluation results showed that our light-weight scheme could considerably reduce the traffic, reaching a sub-optimal result. In addition, the performance gain is obtained with a computation overhead of just a few seconds. The evaluation results also showed that the hybrid caching strategy could follow the rapid variation of the popularity. While a single LFU strategy drops the hit ratio by 13.9%, affected by rapid popularity changes, our proposed hybrid strategy could limit the degradation to only 2.3%