Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining - Concepts and Techniques.

http://emamispnu.persiangig.com/Projects/Cloud%20Computing/science_2.pdf

Review: A survey on security issues in service delivery models of cloud computing

The Grid 2: Blueprint for a New Computing Infrastructure

Recent technology trends in the Web Services (WS) domain indicate that a solution eliminating the presumed complexity of the WS-* standards may be in sight: advocates of REpresentational State Transfer (REST) have come to believe that their ideas explaining why the World Wide Web works are just as applicable to solve enterprise application integration problems and to simplify the plumbing required to build service-oriented architectures. In this paper we objectify the WS-* vs. REST debate by giving a quantitative technical comparison based on architectural principles and decisions. We show that the two approaches differ in the number of architectural decisions that must be made and in the number of available alternatives. This discrepancy between freedom-from-choice and freedom-of-choice explains the complexity difference perceived. However, we also show that there are significant differences in the consequences of certain decisions in terms of resulting development and maintenance costs. Our comparison helps technical decision makers to assess the two integration styles and technologies more objectively and select the one that best fits their needs: REST is well suited for basic, ad hoc integration scenarios, WS-* is more flexible and addresses advanced quality of service requirements commonly occurring in enterprise computing.

/pdf/restful-web-services-vs-big-web-services-making-the-right-2tg7890x7z.pdf

Restful web services vs. "big"' web services: making the right architectural decision

Heterogeneous architectures have experienced a great development thanks to their excellent cost/performance ratio and low power consumption. But heterogeneity significantly complicates both programming and efficient use of the resources. As a result, programmers have ended up using fixed roles for each kind of device: CPUs for sequential and management tasks and GPUs for parallel work. This is a waste of computing power. Maat is a library for OpenCL programmers that allows for the efficient execution of a single data-parallel kernel using all the available devices. It provides the programmer with an abstract view of the system to enable the management of heterogeneous environments regardless of the underlying architecture, and a set of load balancing methods, which perform data distribution. With Maat, programmers only need to develop a data-parallel kernel, select a load balancing method, and run it on the whole system. Experimental results show that Maat efficiently utilizes all the resources, independently of their number and nature. Provided the most appropriate method is selected, Maat is able to achieve a speedup of up to 1.97 using two GPUs with respect to a single GPU and even over 2 when the CPUs, which are much less performant, come into play.

Simplifying programming and load balancing of data parallel applications on heterogeneous systems

This paper presents the Load Balancing for OpenCL (lbcl) library, devoted to automatically solve load balancing issues on both multi-platform and heterogeneous environments. Using this library, a single kernel can be executed on a set of heterogeneous devices, giving each device an amount of work proportional to its computing power. A wrapper has been developed so the library can balance the workload of an existing application not only without introducing any changes into its source code, but without any recompilation stage. Also a general OpenCL profiler has been developed to easily do a detailed profiling of the obtained results.

Static Multi-device Load Balancing for OpenCL

Study of neural net training methods in parallel and distributed architectures

This paper presents an extension of the AMBLE model, an awareness model which manage load balancing by means of a multi-agent based architecture, with the aim to establish a cooperative load balancing model for collaborative grid environments This model, named C-AMBLE (Cooperative Awareness Model for Balancing the Load in grid Environments) applies some theoretical principles of multi-agents systems, awareness models, and third party models, to promote an efficient autonomous cooperative task delivery in grid environments This cooperative task management, implemented using web services, has been tested in a real and heterogeneous grid infrastructure with very successful results This paper presents some of these outcomes while emphasizing on the performance speedup of the system using this model

An agents-based cooperative awareness model to cover load balancing delivery in grid environments

Heterogeneous systems are the core architecture of most of the high-performance computing nodes, due to their excellent performance and energy efficiency. However, a key challenge that remains is programmability, specifically, releasing the programmer from the burden of managing data and devices with different architectures. To this end, we extend EngineCL to support FPGA devices. Based on OpenCL, EngineCL is a high-level framework providing load balancing among devices. Our proposal fully integrates FPGAs into the framework, enabling effective cooperation between CPU, GPU, and FPGA. With command overlapping and judicious data management, our work improves performance by up to 96% compared with single-device execution and delivers energy-delay gains of up to 37%. In addition, adopting FPGAs does not require programmers to make big changes in their applications because the extensions do not modify the user-facing interface of EngineCL.

/pdf/cooperative-cpu-gpu-and-fpga-heterogeneous-execution-with-qmfac9nuhd.pdf

José Luis Bosque

Papers

Simplifying programming and load balancing of data parallel applications on heterogeneous systems

Static Multi-device Load Balancing for OpenCL

Study of neural net training methods in parallel and distributed architectures

An agents-based cooperative awareness model to cover load balancing delivery in grid environments

Cooperative CPU, GPU, and FPGA heterogeneous execution with EngineCL