In this video from PyData NYC 2012, Andreas Klöckner from New York University presents a brief introduction to GPU programming with Python, including run-time code generation and use of high-level tools like PyCUDA, PyOpenCL, and Loo.py.
…today’s situation is clear: HPC is struggling with reliability at scale. Well over 10 years ago, Google proved that commodity hardware was both cheaper and more effective for hyperscale processing when controlled by software-defined systems, yet the HPC market persists with its old-school, hardware-based paradigm. Perhaps this is due to prevailing industry momentum or working within the collective comfort zone of established practices. Either way, hardware-centric approaches to storage resiliency need to go.
Many new technologies used in High Performance Computing (HPC) have allowed new application areas to become possible. Advances like multi-core, GPU, NVMe, and others have created application verticals that include accelerator assisted HPC, GPU based Deep Learning, Fast storage and parallel file systems, and Big Data Analytics systems. In this special insideHPC technology guide sponsored by our friends over at Tyan, we look at practical hardware design strategies for modern HPC workloads.
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