Special Systems Design and Implementation / Intel Science & Technology Center Seminar

Wednesday, March 29, 2017 -
10:00am to 11:30am


Panther Hollow Conference Room, 4th Floor Robert Mehrabian Collaborative Innovation Center



Event Website:


For More Information, Contact:


Discrete GPUs provide massive parallelism to support today’s most interesting high throughput workloads such as deep learning, computational finance, and visual analytics. Intel is making strides in increasing the capability of the GPU on the SoC to support these workloads and there are cases where an integrated GPU can be a compelling solution with a lower total cost of ownership for GPGPU computing.  In this talk we will go into the architectural details of the GPGPU Architecture of Intel Processor Graphics and address the question: How do I program the full teraflop GPU integrated with my CPU?—Adam Lake is a member of Intel’s GPGPU architecture team with a current focus on Compute/GPGPU Architecture. He represented Intel for OpenCL 1.2 and 2.0 and was instrumental in the design of features including shared virtual memory, device side enqueue, improving the execution and memory models, and driving support for an intermediate representation. He was a Sr. Software Architect on Larrabee, now known as Xeon Phi, and has over 40 patents or patents pending. Adam worked previously in non-photorealistic rendering and the design of stream programming systems which included the implementation of simulators, assemblers, and compilers. He did his undergraduate work at the University of Evansville, his graduate studies at UNC Chapel Hill, and spent time at Los Alamos National Laboratory. He has been a co-author on 2 SIGGRAPH papers, numerous book chapters and other peer reviewed papers in the field of computer graphics, and was the editor of Game Programming Gems 8.Girish Ravunnikutty is a member of  GPGPU architecture team at Intel. During his career at Intel, Girish’s major focus has been GPU compute performance analysis and path finding features for future GPU architectures. His analysis and optimizations efforts led to multiple software design wins for Intel Graphics. Girish architected the first OpenCL performance analysis tool from Intel. Before joining Intel, Girish worked with Magma Design Automation and IBM labs. He did his Master’s specializing in GPU Compute at University of Florida, Gainesville, and he worked with Oakridge National Laboratories accelerating Particle in cell algorithm on GPU’s.Visitor Host: Mike Kozuch


Seminar Series