In this podcast, the Radio Free HPC team looks at D-Wave’s new open source software for quantum computing. The software is available on github along with a whitepaper written by Cray Research alums Mike Booth and Steve Reinhardt. “The new tool, qbsolv, enables developers to build higher-level tools and applications leveraging the quantum computing power of systems provided by D-Wave, without the need to understand the complex physics of quantum computers.”
“Intel recently announced the first product release of its High Performance Python distribution powered by Anaconda. The product provides a prebuilt easy-to-install Intel Architecture (IA) optimized Python for numerical and scientific computing, data analytics, HPC and more. It’s a free, drop in replacement for existing Python distributions that requires no changes to Python code. Yet benchmarks show big Intel Xeon processor performance improvements and even bigger Intel Xeon Phi processor performance improvements.”
“Just as a software ecosystem helped to create the immense computing industry that exists today, building a quantum computing industry will require software accessible to the developer community,” said Bo Ewald, president, D-Wave International Inc. “D-Wave is building a set of software tools that will allow developers to use their subject-matter expertise to build tools and applications that are relevant to their business or mission. By making our tools open source, we expand the community of people working to solve meaningful problems using quantum computers.”
While HPC developers worry about squeezing out the ultimate performance while running an application on dedicated cores, Intel TBB tackles a problem that HPC users never worry about: How can you make parallelism work well when you share the cores that you run upon?” This is more of a concern if you’re running that application on a many-core laptop or workstation than a dedicated supercomputer because who knows what will also be running on those shared cores. Intel Threaded Building Blocks reduce the delays from other applications by utilizing a revolutionary task-stealing scheduler. This is the real magic of TBB.
A new site developed by Tin H compares the HPC virtualization capabilities of Docker, Singularity, Shifter, and Univa Grid Engine Container Edition. “They bring the benefits of container to the HPC world and some provide very similar features. The subtleties are in their implementation approach. MPI maybe the place with the biggest difference.”
“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.”
With modern processors that contain a large number of cores, to get maximum performance it is necessary to structure an application to use as many cores as possible. Explicitly developing a program to do this can take a significant amount of effort. It is important to understand the science and algorithms behind the application, and then use whatever programming techniques that are available. “Intel Threaded Building Blocks (TBB) can help tremendously in the effort to achieve very high performance for the application.”
Today ORNL announced the full schedule of 2017 GPU Hackathons at multiple locations around the world. “The goal of each hackathon is for current or prospective user groups of large hybrid CPU-GPU systems to send teams of at least 3 developers along with either (1) a (potentially) scalable application that could benefit from GPU accelerators, or (2) an application running on accelerators that need optimization. There will be intensive mentoring during this 5-day hands-on workshop, with the goal that the teams leave with applications running on GPUs, or at least with a clear roadmap of how to get there.”
Manuel Arenaz from Appentra presented this talk at the OpenMP booth at SC16. “Parallware is a new technology for static analysis of programs based on the production-grade LLVM compiler infrastructure. Using a fast, extensible hierarchical classification scheme to address dependence analysis, it discovers parallelism and annotates the source code with the most appropriate OpenMP & OpenACC directives.”
The Seventh International Workshop on Accelerators and Hybrid Exascale Systems (AsHES) has issued its Call for Papers. The event takes place May 29 in Orlando, Florida in conjunction with the IEEE International Parallel and Distributed Processing Symposium.