Mark Silberstein
 

The Scalable Parallel Computing Lab's SPCL_Bcast seminar continues with Mark Silberstein of Technion presenting on The evolution of accelerator-centric GPU services - past, present, future.. Everyone is welcome to attend (over Zoom)!

When: Thursday, 28th November, 6PM CET

Where: Zoom

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Abstract: GPUs have come a long way, evolving from gaming processors to the main driving force behind modern AI systems. However, from a system design perspective, they remain co-processors: they cannot operate independently of the host CPU, which is necessary to invoke kernels, manage GPU memory, perform data transfers, and interact with I/O devices. Thus, beyond the complexity of optimizing individual kernels, GPU-accelerated application development faces fundamental challenges in integrating GPU computations into complex data and control flows involving networking and storage. Since 2013, my students in the Accelerated Computing Systems Group (https://acsl.group) have been exploring an alternative, accelerator-centric system design in which a GPU runs specially crafted OS layers that allow GPU kernels to access files, storage devices, SmartNICs, and network services, without CPU involvement in the data and/or control path. We have demonstrated how such an approach simplifies the programming burden and achieves high performance. In this talk, I will survey the key ideas of the accelerator-centric design, discuss the main takeaways, and explore future trends.

Biography: Mark Silberstein is a professor in the Electrical and Computer Engineering Department at the Technion - Israel Institute of Technology. His research interests span a broad range of topics in computer systems, including OS, networking, computer architecture, and systems security. His projects have been published in top systems venues, with some winning awards and others being adopted by the industry. He regularly serves on the program committees of leading systems conferences, including as a program co-chair of Eurosys '24 and ASPLOS '26.

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Scalable Parallel Computing Lab (SPCL)
Department of Computer Science, ETH Zurich
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