Ketan Mandke
Ketan is a software engineer at Google, where he works on making machine learning more accessible to programmers. He previously worked on mmWave wireless networks and mobile networking within Alphabet as well. Prior to joining Google, Ketan implemented software defined radio systems and developed signal processing algorithms for detection and estimation in geolocation applications. Ketan holds a Ph.D. in Electrical Engineering from the University of Texas at Austin.
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A Flexible Approach to Autotuning Multi-Pass Machine Learning Compilers
Berkin Ilbeyi
Bjarke Roune
Blake Hechtman
Emma Wang
Karthik Srinivasa Murthy
Mike Burrows
Nikhil Sarda
Rezsa Farahani
Samuel J. Kaufman
Shen Wang
Sudip Roy
Yuanzhong Xu
PACT (2021)
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Search-based techniques have been demonstrated effective in solving complex optimization problems that arise in domain-specific compilers for machine learning (ML). Unfortunately, deploying such techniques in production compilers is impeded by two limitations. First, prior works require factorization of a computation graph into smaller subgraphs over which search is applied. This decomposition is not only non-trivial but also significantly limits the scope of optimization. Second, prior works require search to be applied in a single stage in the compilation flow, which does not fit with the multi-stage layered architecture of most production ML compilers.
This paper presents XTAT, an autotuner for production ML compilers that can tune both graph-level and subgraph-level optimizations across multiple compilation stages. XTAT applies XTAT-M, a flexible search methodology that defines a search formulation for joint optimizations by accurately modeling the interactions between different compiler passes. XTAT tunes tensor layouts, operator fusion decisions, tile sizes, and code generation parameters in XLA, a production ML compiler, using various search strategies. In an evaluation across 150 ML training and inference models on Tensor Processing Units (TPUs) at Google, XTAT offers up to 2.4x and an average 5% execution time speedup over the heavily-optimized XLA compiler.
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In this paper we describe an application of Temporospatial SDN (TS-SDN) to UAV networks. Airborne platforms (airplanes, balloons, airships) can be used to carry wireless communication nodes to provide direct-to-user as well as backhaul connections. Such networks also include ground nodes typically equipped with highly directional steerable transceivers. The physics of flight as well as state of the atmosphere lead to time-dynamic link metrics and availability. As nodes move around, the network topology and routing need to adjust to maintain connectivity. Further, mechanical aspects of the system, such as time required to mechanically steer antennas, makes the reactive repair approach more costly than in terrestrial applications. Instead, TS-SDN incorporates reasoning about physical evolution of the system to proactively adjust the network topology in anticipation of future changes. Using airborne networks under development at Google as an example, we discuss the benefits of the TS-SDN approach compared to reactive repair in terms of network availability. We also identify additional constraints one needs to account for when computing the network topology, such as non-interference with other stationary and moving sources. Existing SDN standards do not support scheduled updates necessary in a TS-SDN. We describe our extensions to control messages and software implementation used in field tests.
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