Twittered from jhlagado, a link to training at Google Code University on parallel programming with MapReduce. All HTML-y, for easy reading on your iPhone during the commute, and with example code.
In this guest article, our friends at Intel discuss how accelerated computing has diversified over the past several years given advances in CPU, GPU, FPGA, and AI technologies. This innovation drives the need for an open and cross-platform language that allows developers to realize the potential of new hardware, minimizes development cost and complexity, and maximizes reuse of their software investments.
Not too long ago, building a converged HPC/AI environment - with two domains: High Performance Computing (HPC) and Artificial Intelligence (AI) - would require spending a lot of money on proprietary systems and software with the hope that it would scale as business demands changed. In this insideHPC technology guide, as we’ll see, by relying on open source software and the latest high performance/low cost system architectures, it is possible to build scalable hybrid on-premises solutions that satisfy the needs of converged HPC/AI workloads while being robust and easily manageable.
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