Chuan-Lin Lai

A parallel loop self-scheduling on grid computing environments

Internet computing and grid technologies promise to change the way we tackle complex problems. They will enable large-scale aggregation and sharing of computational, data and other resources across institutional boundaries. And harnessing these new technologies effectively will transform scientific disciplines ranging from high-energy physics to the life sciences. In this paper, a grid computing environment is proposed and constructed on multiple PC clusters by using Globus Toolkit (GT) and SUN Grid Engine (SGE). The experimental results are also conducted by using the matrix multiplication to demonstrate the performance. On the other hand, the approaches to deal with scheduling and load balancing on multiple heterogeneous PC clusters computer system are not mature. Self-scheduling schemes which are suitable for parallel loops with independent iterations on heterogeneous cluster computer system have been designed in the past. However, these schemes, such as FSS, GSS and TSS, can not achieve load balancing in extremely heterogeneous environment. We propose a heuristic approach based upon a two-phase scheme to solve parallel regular loop scheduling problem on an extremely heterogeneous grid computing environment.

A Resource Broker for Computing Nodes Selection in Grid Computing Environments

As Grid Computing is becoming a reality, there is a need for manag- ing and monitoring the available resources worldwide, as well as the need for conveying these resources to the everyday user. This paper describes a resource broker with its main function as to match the available resources to the users needs. The use of the resource broker provides a uniform interface to access any of the available and appropriate resources using users credentials. The resource broker runs on top of the Globus Toolkit. Therefore, it provides security and current information about the available resources and serves as a link to the di- verse systems available in the Grid.

Designing computing platform for BioGrid

Grid environment allows sharing of a large amount of idle resources of compute and other available information. Accordingly, we have set up the BioGrid for computing resources by combining grid technology with parallel bioinformatics applicable software in erecting the BioGrid computing platform. In one instance for example, biology data which is heterogeneous and grows rapidly, we plan to integrate and share it using grid technology. In this paper, the FASTA applicable software is used to test and analyse the grid environment to reduce time and improve efficacy. The experimental results and performances of the bioinformatics tool use on the grid system are also presented in this paper.

On utilization of the grid computing technology for video conversion and 3d rendering

In this paper, we investigate the recent popular computing technique called Grid Computing, and use video conversion and 3D rendering applications to demonstrate this technology’s effectiveness and high performance. We also report on developing a resource broker called Phantom that runs on our grid computing testbed and whose main function is querying nodes in grid computing environments and showing their system information to aid in selecting the best nodes for job assignments to have the jobs executed in the least amount of time.

Construct a grid computing environment for bioinformatics

Internet computing and grid technologies promise to change the way we tackle complex problems. They will enable large-scale aggregation and sharing of computational, data and other resources across institutional boundaries. And harnessing these new technologies effectively will transform scientific disciplines ranging from high-energy physics to the life sciences. The computational analysis of biological sequences is a kind of computation driven science. Cause the biology data growing quickly and these databases are heterogeneous. We can use the grid system sharing and integrating the heterogeneous biology database. As we know, bioinformatics tools can speed up analysis the large-scale sequence data, especially about sequence alignment. The FASTA is a tool for aligning multiple protein or nucleotide sequences. FASTA which we used is a distributed and parallel version. The software uses a message-passing library called MPl (Message Passing Interface) and runs on distributed workstation clusters as well as on traditional parallel computers. A grid computing environment is proposed and constructed on multiple Linux PC clusters by using Globus Toolkit (GT) and SUN Grid Engine (SGE). The experimental results and performances of the bioinformatics tool using on grid system are also presented in this paper.

Apply cluster and grid computing on parallel 3D rendering

A cluster is a collection of independent and cheap machines, used together as a supercomputer to provide a solution. In this paper, a PC cluster consisting of one master node and nine disk-less slave nodes (10 processors), is proposed and built for parallel rendering purposes. The system architecture and benchmark performances of this cluster are also presented. Internet computing and grid technologies promise to change the way we tackle complex problems. They will enable large-scale aggregation and sharing of computational, data and other resources across institutional boundaries. Harnessing these new technologies effectively will transform scientific disciplines ranging from high-energy physics to the life sciences. Also, in this paper, we construct two heterogeneous PC clusters for parallel rendering purpose and install Linux Red Hat 9 on each PC cluster. Then, these clusters are set to the different subnet. Therefore, we use the grid middleware lambdaobus ToolKit, to connect these two clusters to form a grid computing environment on multiple Linux PC clusters. We also install the SUN Grid Engine, to manage and monitor incoming or outgoing computing jobs and schedule the job to achieve high performance computing and high CPU utilization. The system architecture and benchmark performances of this cluster are also presented

Design and implementation of a computational grid for bioinformatics

The popular technologies, Internet computing and grid technologies promise to change the way we tackle complex problems. They enable large-scale aggregation and sharing of computational, data and other resources across institutional boundaries. And harnessing these new technologies effectively transforms scientific disciplines ranging from high-energy physics to the life sciences. The computational analysis of biological sequences is a kind of computation driven science. Cause the biology data growing quickly and these databases are heterogeneous. We can use the grid system sharing and integrating the heterogeneous biology database. As we know, bioinformatics tools can speed up analysis the large-scale sequence data, especially about sequence alignment and analysis. The FASTA is a tool for aligning multiple protein or nucleotide sequences. These two bioinformatics software, which we used is a distributed and parallel version. The software uses a message-passing library called MPI (message passing interface) and runs on distributed workstation clusters as well as on traditional parallel computers. A grid computing environment is proposed and constructed on multiple Linux PC clusters by using globus toolkit (GT) and SUN grid engine (SGE). The experimental results and performances of the bioinformatics tool using on grid system are also presented.

The Implementation of a GPU-Accelerated Virtual Desktop Infrastructure Platform

This work focuses on how to provide the best virtual desktop technology, virtualization performance and hardware in the computer classroom. There are using the three popular virtualization technologies in the same environment to establish the same conditions, pure performance comparison: VMware Horizon, Citrix XenDesktop, and Microsoft VDI. Test software used to the current popular software, system performance including a test CPU and RAM PassMark Performance Test,and observe the vCenter Server performance changes and record statistics, to determine the advantages and disadvantages of various virtualization technologies. Finally, hardware support exclusive GPU virtualization product - Nvidia GRID K2 through GPU virtualization technology sharing and vGPU to carry on VMware vSphere, via Cinebench, YouTube 1080p high-quality video for bandwidth flow test, and use 3DMark Fire Strike Extreme to enhance the performance of virtual desktop, and bring users to enjoy higher-quality cloud services. The results of this study show that virtual desktop technologies are good in different field. On the other hand, the hardware performance breakthrough Moore’s Law of this era, the single CPU has more number of cores, and the memory capacity of the larger single,and provided by the GPU for graphics acceleration processing capabilities, the performance of server has been perfect than the previous. Therefore, the quality of cloud services is also greatly improved. The study shows that the virtual test can be imported into the classroom through the stress test in the experiment. To make sure the its performance and cost are really suitable for classroom imported desktop virtualization system.