NVIDIA GPU Technology Workshop in SE Asia
CenterSpace will be giving a presentation at the upcoming GPU Technology Workshop South East Asia on July 10. The conference will be held at the Suntec Singapore Convention & Exhibition Centre. For a full schedule of talks see the agenda.
From CPU to GPU: a comparative case study / Andy Gray – CenterSpace Software
In this code-centric presentation, we will compare and contrast several approaches to a simple algorithmic problem: a straightforward implementation using managed code, a multi-CPU approach using a parallelization library, coupling object-oriented managed abstractions with high-performance native code, and seamlessly leveraging the power of a GPU for massive parallelization without code changes.
Andy Gray, a technology evangelist for CenterSpace Software, will be delivering the talk. We hope to see you there!
Parallel Computing in Finance Lecture
The June 5-6 conference at the University of Chicago titled, Recent Developments in Parallel Computing in Finance hosted talks by various academics in finance, Microsoft, Intel, and CenterSpace. CenterSpace was invited to give a two hour lecture and tutorial on GPU computing at the Stevanovich Center at the University of Chicago. We will post up the tutorial video from the talk as soon as it becomes available.
Lecture by Trevor MisfeldtCenterSpace Software, a leading provider of numerical component libraries for the .NET platform, will give an overview of their NMath math and statistics libraries and how they are being used in industry. The Premium Edition of NMath offers GPU parallelization. Xeon Phi, C++ AMP and CUDA are technologies of interest. Support for each will be discussed. Also discussed will be CenterSpace’s Adapative Bridge™ technology, which provides intelligent, adaptive routing of computations between CPU and GPUs. The presentation will finish with a demonstration followed by performance charts.
Tutorial by Andy Gray
In this hands-on programming tutorial, we will compare and contrast several approaches to a simple algorithmic problem: a straightforward implementation using managed code, a multi-CPU approach using a parallelization library, coupling object-oriented managed abstractions with high-performance native code, and seamlessly leveraging the power of a GPU for massive parallelization.