Ranga Siriwardena's Blog

Ubuntu 10.04 shows window buttons in the top left side of the window. Some people are preferred this new change but others are not. According to the preference of the user this can be changed easily. Just use following simple steps. Open the “run application window” ( Alt + F2) Type “gconf-editor” in text area and click on run to start “Configuration Editor” Go to /apps/metacity/general Double click on button_layout (Now You can edit the value of button layout) Replace “:minimize,maximize,close” with “close,minimize,maximize:” ( Now You are done..., : denotes the side to appear the window buttons)

WSO2 , the leading, innovative and open source middleware technology provider introduced Stratos , the first ever 100% open source cloud platform for creating and composing enterprise service-oriented architecture (SOA) services and composite applications. Stratos is based on the award wining WSO2 Carbon platform and it provides all the capabilities of WSO2 carbon in a platform as a service (PaaS). So Stratos provides Cloud Services for all the carbon related products as listed below. WSO2 Enterprise Service Bus as a Service WSO2 Application Server as a Service WSO2 Data as a Service WSO2 Governance as a Service WSO2 Identity as a Service WSO2 Business Activity Monitoring as a Service WSO2 Business Processes as a Service WSO2 Business Rules as a Service WSO2 Mashups as a Service WSO2 Gadgets as a Service  It is important to note the capabilities and features of Stratos. It provides automatic governance and monitoring, centralized identity management, multi-tenancy...

OpenCL is a standardized, cross platform, parallel computing API which allows to development of portable parallel applications for the systems with heterogeneous devices. It also based on C language like CUDA and it standardizes the parallel programming approach for rapidly growing various parallel computing platforms. For an example if we develops an application with CUDA then it is harder to use that application with different hardware platform other than Nvidia. But OpenCL eliminates this issue of vendor specificity with a more complex platform and device management model. With this multi vendor portability OpenCL device management model, kernel compilation model and kernel launch are really harder than the CUDA counterpart. But this standardization model will leads to more improvements on parallel computing with heterogeneous devices. References:  OpenCL Programming Guide for the CUDA Architecture Version 3.1, 2010. NVIDIA CUDA Programming Guide Version 2.3.1, 2009.

Nvidia introduced their new general purpose parallel computing architecture called CUDA in year 2006. Which provides a new parallel programming model and instruction set architecture for Nvidia GPUs. Also it comes with a software environment that allows programmer to use C as high level programming language and solve computationally demanding problems in a more efficient way. A hierarchy of thread groups, barrier synchronization and shared memories are the three key abstractions provided by CUDA that are simply exposed to the developer as a minimal set of language extensions. They give thread parallelism and fine-grained data parallelism, nested within task parallelism and coarse grained data parallelism. And also this abstractions helps the developer to partition the task into coarse subtasks which can be solved independently in parallel, and then into finer pieces that can be solved cooperatively in parallel. With CUDA large numbers of processor cores can be used to transparently sc...

Over the past few years GPU (Graphics Processing Unit) becomes competitive computing hardware against the CPU (Central Processing Unit) because of its rapid increasing performance and capabilities. Recent improvements of GPU’s highly parallel programming capabilities lead to mapped wide variety of general purpose complex application with tremendous performance improvements. This attempt on GPU is also called as General Purpose Computation on Graphics Processor ( GPGPU ) and this feature leads GPU to the next generation of high performance computing Despite the relatively recent introduction of General Purpose GPU hardware, various kinds of applications have begun to take the advantage of GPU. GPU’s high performance techniques proven that it can be used successfully in diverse variety of application areas such as image processing, video processing, scientific computing, bioinformatics, computer vision, neural networks, database operations etc. Because of this reason many researches ar...

This was my final year Research & Development individual project and From this research work I was used the massively parallel architecture of GPU as a parallel computing solution, for the improvement of one of the computationally intensive general purpose applications. With that concept I was able to improve dynamic programming based Global genetic Sequence Alignment algorithm with efficient use of heterogeneous memory hierarchy of GPU & CPU. For this research work, CUDA was used because it provides a parallel programming model and an instruction set architecture for Nvidia GPU with an extension of C language. With this work we use two levels of implementations for Needleman-Wunsch Global sequence alignment algorithm. First implementation was based on  global memory of GPU and the other one is based on the fast, on-chip, Shared memory of GPU. Use of shared memory improved the performance of the implementation with the blocking strategy for the Needleman-Wunsch algorithm. ...