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Parallel Programming and Computing Platform

Parallel Programming and Computing Platform
What is CUDA? Enroll today! Intro to Parallel Programming An open, online course from Udacity Instructors: Dr. CUDA® is a parallel computing platform and programming model invented by NVIDIA. With millions of CUDA-enabled GPUs sold to date, software developers, scientists and researchers are finding broad-ranging uses for GPU computing with CUDA. Identify hidden plaque in arteries: Heart attacks are the leading cause of death worldwide. Analyze air traffic flow: The National Airspace System manages the nationwide coordination of air traffic flow. Visualize molecules: A molecular simulation called NAMD (nanoscale molecular dynamics) gets a large performance boost with GPUs. Background GPU Computing: The Revolution You're faced with imperatives: Improve performance. Not anymore. Using high-level languages, GPU-accelerated applications run the sequential part of their workload on the CPU – which is optimized for single-threaded performance – while accelerating parallel processing on the GPU. Related:  Massive Parallel Processing Tools

Main Page CUDA CUDA is a parallel computing platform and application programming interface (API) model created by Nvidia.[1] It allows software developers and software engineers to use a CUDA-enabled graphics processing unit (GPU) for general purpose processing — an approach termed GPGPU (General-Purpose computing on Graphics Processing Units). The CUDA platform is a software layer that gives direct access to the GPU's virtual instruction set and parallel computational elements, for the execution of compute kernels.[2] Background[edit] The graphics processing unit (GPU), as a specialized computer processor, addresses the demands of real-time high-resolution 3D graphics compute-intensive tasks. Programming abilities[edit] Example of CUDA processing flow Copy data from main memory to GPU memoryCPU initiates the GPU compute kernelGPU's CUDA cores execute the kernel in parallelCopy the resulting data from GPU memory to main memory CUDA provides both a low level API and a higher level API. Advantages[edit]

Course on CUDA Programming Course on CUDA Programming on NVIDIA GPUs, July 22--26, 2013 The course will be given again in Oxford in July 2014, almost certainly the week of July 21-25. We hope to confirm this and have the online registration / payment system set up by early May. This is a 5-day hands-on course for students, postdocs, academics and others who want to learn how to develop applications to run on NVIDIA GPUs using the CUDA programming environment. The course consists of approximately 3 hours of lectures and 4 hours of practicals each day. Costs for the course will be as follows: £100 for those from Oxford University and other members of the e-Infrastructure South consortium (Bristol, Southampton, STFC and UCL) £200 for those from other UK universities £500 for those from other government labs, not-for-profit organisations, and foreign universities £2000 for those from industry (this will include lunch each day) To encourage early registration, the costs will increase by 50% after July 4th. Venue

Hugin - Panorama photo stitcher Methodologies for Network-level Optimization of Cluster Computers supervisors: Dr Peter Strazdins A Beowulf-style cluster computer is a parallel computer using Commercial-off-the-Shelf switch-based network to communicate between the processors. The ANU Beowulf cluster Bunyip is such a cluster based on Fast (100Mb) Ethernet switches. Clusters have proved a highly cost-effective high performance computer model, and have largely displaced the traditional massively parallel computers built with expensive vendor-supplied networks. The switch-based networks used by clusters not only support normal point-to-point messaging (via TCP/IP in the case of Ethernet networks), but data transfers by collective communications (e.g. multicasts) and remote direct memory access (RDMA). In partnership with the local company Alexander Technology, this project will investigate evaluating the effectiveness of various hardware and software configuration options for various networks. Hardware configuration options include using multiple NICS (channels).

OpenVIDIA - OpenVIDIA The Khronos Group Inc. High Performance Computing - HPC Cloud Computing P2 instances are ideally suited for machine learning, engineering simulations, computational finance, seismic analysis, molecular modeling, genomics, rendering, high performance databases, and other GPU compute workloads. P2 instance offers 16 NVIDIA K80 GPUs with a combined 192 Gigabytes (GB) of video memory, 40,000 parallel processing cores, 70 teraflops of single precision floating point performance, over 23 teraflops of double precision floating point performance, and GPUDirect technology for higher bandwidth and lower latency peer-to-peer communication between GPUs. P2 instances also feature up to 732 GB of host memory, up to 64 vCPUs using custom Intel Xeon E5-2686 v4 (Broadwell) processors, dedicated network capacity for I/O operation, and enhanced networking through the Amazon EC2 Elastic Network Adaptor. Visit the User Guide >>

Introduction to Parallel Programming With CUDA When does the course begin? This class is self paced. You can begin whenever you like and then follow your own pace. It’s a good idea to set goals for yourself to make sure you stick with the course. How long will the course be available? This class will always be available! How do I know if this course is for me? Take a look at the “Class Summary,” “What Should I Know,” and “What Will I Learn” sections above. Can I skip individual videos? Yes! How much does this cost? It’s completely free! What are the rules on collaboration? Collaboration is a great way to learn. Why are there so many questions? Udacity classes are a little different from traditional courses. What should I do while I’m watching the videos? Learn actively!