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CUDA (Compute Unified Device Architecture) is a parallel computing platform and programming model created by NVIDIA and implemented by the graphics processing units (GPUs) that they produce.[1] CUDA gives program developers direct access to the virtual instruction set and memory of the parallel computational elements in CUDA GPUs. Using CUDA, the GPUs can be used for general purpose processing (i.e., not exclusively graphics); this approach is known as GPGPU. Unlike CPUs, however, GPUs have a parallel throughput architecture that emphasizes executing many concurrent threads slowly, rather than executing a single thread very quickly. CUDA provides both a low level API and a higher level API. Example of CUDA processing flow 1. Background[edit] The GPU, as a specialized processor, addresses the demands of real-time high-resolution 3D graphics compute-intensive tasks. Advantages[edit] CUDA has several advantages over traditional general-purpose computation on GPUs (GPGPU) using graphics APIs: Related:  Computer Vision

OpenCL Open Computing Language (OpenCL) is a framework for writing programs that execute across heterogeneous platforms consisting of central processing units (CPUs), graphics processing units (GPUs), digital signal processors (DSPs), field-programmable gate arrays (FPGAs) and other processors. OpenCL includes a language (based on C99) for writing kernels (functions that execute on OpenCL devices), plus application programming interfaces (APIs) that are used to define and then control the platforms. OpenCL provides parallel computing using task-based and data-based parallelism. OpenCL is an open standard maintained by the non-profit technology consortium Khronos Group. It has been adopted by Apple, Intel, Qualcomm, Advanced Micro Devices (AMD), Nvidia, Altera, Samsung, Vivante and ARM Holdings. For example, OpenCL can be used to give an application access to a graphics processing unit for non-graphical computing (see general-purpose computing on graphics processing units). History[edit]

Staged event-driven architecture SEDA employs dynamic control to automatically tune runtime parameters (such as the scheduling parameters of each stage) as well as to manage load (like performing adaptive load shedding). Decomposing services into a set of stages also enables modularity and code reuse, as well as the development of debugging tools for complex event-driven applications. See also[edit] References[edit] Bibliography[edit] External links[edit] Apache ServiceMix provides a Java SEDA wrapper, combining it with related message architectures (JMS, JCA & straight-through flow).Criticism about how SEDA premises (threads are expensive) are no longer validJCyclone: Java open source implementation of SEDAMule ESB is another open-source Java implementationSEDA: An Architecture for Highly Concurrent Server Applications describing the PhD thesis by Matt Welsh from Harvard UniversityA Retrospective on SEDA by Matt Welsh, July 26, 2010

GPGPU General-purpose computing on graphics processing units (GPGPU, rarely GPGP or GP²U) is the utilization of a graphics processing unit (GPU), which typically handles computation only for computer graphics, to perform computation in applications traditionally handled by the central processing unit (CPU).[1][2][3] Any GPU providing a functionally complete set of operations performed on arbitrary bits can compute any computable value. Additionally, the use of multiple graphics cards in one computer, or large numbers of graphics chips, further parallelizes the already parallel nature of graphics processing.[4] OpenCL is the currently dominant open general-purpose GPU computing language. The dominant proprietary framework is Nvidia's CUDA.[5] Programmability[edit] In principle, any boolean function can be built-up from a functionally complete set of logic operators. DirectX 9 Shader Model 2.x suggested the support of two precision types: full and partial precision. Stream processing[edit]

Signal processing and the evolution of NAND flash memory Fueled by rapidly accelerating demand for performance-intensive computing devices, the NAND flash memory market is one of the largest and fastest-growing segments of the semiconductor industry, with annual sales of nearly $20 billion. During the past decade, the cost per bit of NAND flash has declined by a factor of 1,000, or a factor of 2 every 12 months, far exceeding Moore’s Law expectations. This rapid price decline has been driven by aggressive process geometry scale-down and by an increase in the number of bits stored in each memory cell from one to two and three bits per cell. As a consequence, the endurance of flash memory – defined as the number of Program and Erase (P/E) cycles that each memory cell can tolerate throughout its lifetime – is severely degraded due to process and array impairments, resulting in a nonlinear increase in the number of errors in flash memory. Getting past errors The most commonly used ECCs for flash memory are Bose-Chaudhuri-Hocquenghem (BCH) codes.

COMPUTE Apache Cassandra Apache Cassandra is an open source distributed database management system designed to handle large amounts of data across many commodity servers, providing high availability with no single point of failure. Cassandra offers robust support for clusters spanning multiple datacenters,[1] with asynchronous masterless replication allowing low latency operations for all clients. Cassandra also places a high value on performance. In 2012, University of Toronto researchers studying NoSQL systems concluded that "In terms of scalability, there is a clear winner throughout our experiments. Cassandra achieves the highest throughput for the maximum number of nodes in all experiments Tables may be created, dropped, and altered at runtime without blocking updates and queries.[6] History[edit] Releases after graduation include Licensing and support[edit] Apache Cassandra is an Apache Software Foundation project, so it has an Apache License (version 2.0). Main features[edit] Decentralized Scalability

COMMUNITY Amazon DynamoDB Overview[edit] DynamoDB differs from other Amazon services by allowing developers to purchase a service based on throughput, rather than storage. Although the database will not scale automatically, administrators can request more throughput and DynamoDB will spread the data and traffic over a number of servers using solid-state drives, allowing predictable performance.[1] It offers integration with Hadoop via Elastic MapReduce. In September 2013, Amazon made available a local development version of DynamoDB so developers can test DynamoDB-backed applications locally.[3] Language bindings[edit] References[edit] External links[edit]

OpenCL by Example Comparison of MySQL database engines This is a comparison between the available database engines for the MySQL database management system (DBMS). A database engine (or "storage engine") is the underlying software component that a DBMS uses to create, read, update and delete (CRUD) data from a database. Comparison between InnoDB and MyISAM[edit] InnoDB recovers from a crash or other unexpected shutdown by replaying its logs. MyISAM must fully scan and repair or rebuild any indexes or possibly tables which had been updated but not fully flushed to disk. Notes[edit] External links[edit]

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