“Managing the work on each node can be referred to as Domain parallelism. During the run of the application, the work assigned to each node can be generally isolated from other nodes. The node can work on its own and needs little communication with other nodes to perform the work. The tools that are needed for this are MPI for the developer, but can take advantage of frameworks such as Hadoop and Spark (for big data analytics). Managing the work for each core or thread will need one level down of control. This type of work will typically invoke a large number of independent tasks that must then share data between the tasks.”
Are you shopping for Public Cloud services? A new Public Cloud Services Comparison site gives a service & feature level mapping between the 3 major public clouds: Amazon Web Service, Microsoft Azure & Google Cloud. Published by Ilyas F, a Cloud Solution Architect at Xebia Group, the Public Cloud Services Comparison is a handy reference manual to help anyone to quickly learn the alternate features & services between clouds.
“Run your Windows and Linux HPC applications using high performance A8 and A9 compute instances on Azure, and take advantage of a backend network with MPI latency under 3 microseconds and non-blocking 32 Gbps throughput. This backend network includes remote direct memory access (RDMA) technology on Windows and Linux that enables parallel applications to scale to thousands of cores. Azure provides you with high memory and HPC-class CPUs to help you get results fast. Scale up and down based upon what you need and pay only for what you use to reduce costs.”
The 3rd annual International Workshop on High-Performance Big Data Computing (HPBDC) has issued its Call for Papers. Featuring a keynote by Prof. Satoshi Matsuoka from Tokyo Institute of Technology, the event takes place May 29, 2017 in Orlando, FL.
In this podcast, the Radio Free HPC team honors the Festivus tradition of the annual Airing of Grievances. Our random gripes include: the need for a better HPC benchmark suite, the missed opportunity for ARM servers, the skittish battery in the new Macbook Pro, and a lack of an industry standards body for cloud computing.
In this Intel Chip Chat, Doug Fisher from Intel describes the company’s efforts to accelerate innovation in artificial intelligence. “Fisher talks about Intel’s upstream investments in academia and open source communities. He also highlights efforts including the launch of the Intel Nervana AI Academy aimed at developers, data scientists, academia, and startups that will broaden participation in AI. Additionally, Fisher reports on Intel’s engagements with open source ecosystems to optimize the performance of the most-used AI frameworks on Intel architecture.”
IDC is out with their latest Worldwide High-Performance Technical Server QView report. The QView presents the HPC market from various perspectives, including by competitive segment, vendor, cluster versus non-cluster, geography, and operating system. It also contains detailed revenue and shipment information by HPC models. “The workgroup segment, and especially the departmental segment, substantially ramped up purchases of HPC servers in the period 2012-2015, in tune with the global economic recovery.”
With the advent of heterogeneous computing systems that combine both main CPUs and connected processors that can ingest and process tremendous amounts of data and run complex algorithms, artificial intelligence (AI) technologies are beginning to take hold in a variety of industries. Massive datasets can now be used to drive innovation in industries such as autonomous driving systems, controlling power grids and combining data to arrive at a profitable decision, for example. Read how AI can now be used in various industries using the latest hardware and software.
In this special guest feature, Daniel Gutierrez from insideBIGDATA offers up his 2017 roundup industry predictions from Big Data thought leaders. “AI, ML, and NLP innovations have really exploded this past year but despite a lot of hype, most of the tangible applications are still based on specialized AI and not general AI. We will continue to see new use-cases of such specialized AI across verticals and key business processes. These use-cases would primarily be focused on the evolutionary process improvement side of the digital transformation.”
“Building on HPE IDOL’s history of delivering industry-leading analytics engineered for human data, IDOL Natural Language Question Answering is the industry’s first comprehensive approach to delivering enterprise class answers,” said Sean Blanchflower, vice president of engineering, Big Data Platform, Hewlett Packard Enterprise. “Designed to meet the demanding needs of data-driven enterprises, this new, language-independent capability can enhance applications with machine learning powered natural language exchan