Intel® Threading Building Blocks (TBB) offers a rich and complete approach to expressing parallelism in a C++ program. It is a library that helps you take advantage of multi-core processor performance without having to be a threading expert. Threading Building Blocks is not just a threads-replacement library. It represents a higher-level, task-based parallelism that abstracts platform details and threading mechanisms for scalability and performance.
The Ohio Supercomputer Center provides supercomputing, research and educational resources to a diverse state and national community, including education, academic research, industry and state government. At the Ohio Supercomputer Center, our duty is to empower our clients, partner strategically to develop new research and business opportunities, and lead Ohio's knowledge economy.
The Large Synoptic Survey Telescope (LSST) is a project to build an 8.4m telescope at Cerro Pachon, Chile and survey the entire sky every three days starting around 2014. The scientific goals of the project range from characterizing the population of largish asteroids which are in orbits that could hit the Earth to understanding the nature of the dark energy that is causing the Universe's expansion to accelerate. The application codes, which handle the images coming from the telescope and generate catalogs of astronomical sources, are being implemented in C++, exported to python using swig. The pipeline processing framework allows these python modules to be connected together to process data in a parallel environment.
In some cases, it may be appropriate to process part of a request synchronously, but to finish processing in a forked child depending on the request data. This can be implemented by using a synchronous server and doing an explicit fork in the request handler class handle() method.
This reference is either acquired through a stringified URI string, NameService lookup (similar to DNS), or passed-in as a method parameter during a call. Object references are lightweight objects matching the interface of the real object (remote or local). Method calls on the reference result in subsequent calls to the ORB and blocking on the thread while waiting for a reply, success or failure. The parameters, return data (if any), and exception data are marshaled internally by the ORB according the local language and OS mapping. [edit]
Distributed Sage is a framework that allows one to do distributed computing from within Sage. It includes a server, client and workers as well as a set of classes that one can subclass from to write distributed computation jobs. It is designed to be used mainly for ‘coarsely’ distributed computations, i.e., computations where jobs do not have to communicate much with each other. This is also sometimes referred to as ‘grid’ computing.
Ack. Ppython requires worker threads on each cluster node. I want an ssh private key (no p/w) solution. 1) Start parallel python execution server on all your remote computational nodes:
Distributed Sage allows you to do distributed computing in Sage. To get up and running quickly, run dsage.setup() to run the configuration utility. Note that configuration files will be stored in the directory $DOT Sage/dsage. QUICK-START