Sundar Dev
Sundar is a performance engineer in the Systems and Services Infrastructure Org at Google. He works on improving efficiency and increasing performance of the distributed compute infrastructure that enables Google's user facing software services like Websearch, Gmail, YouTube, Maps, Ads, Workspace, Google Cloud, etc. His technical interests include computer architecture, distributed and parallel processing systems, hardware/software co-design, and machine learning. He joined Google in 2015 after receiving his M.S. in Electrical and Computer Engineering from Georgia Tech.
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JAXBench: Benchmarking Autonomous TPU Kernel Optimization
Vijay Janapa Reddi
Charles Hong
Arya Tschand
Julian Walker
Suvinay Subramanian
Shangkun Wang
Sethu Sankaran
Nina Cai
2026
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Evaluation benchmarks have driven progress in automated kernel optimization, yet existing suites target GPUs exclusively. We present JAXBench, a TPU-native benchmark for AI-generated kernel optimization on Google Cloud TPUs. JAXBench comprises 50 JAX workloads, including 17 production LLM operators extracted from architectures in the public MaxText library such as Llama-3.1, DeepSeek-V3, Mixtral, Mamba-2, and AlphaFold2, and 33 fused operator sequences adapted from KernelBench. Eight of the 17 production operators ship with hand-optimized Pallas TPU kernels from the public Tokamax library, whose block sizes we tune via grid search, establishing strong reference baselines. We evaluate one-shot generation, iterative coding agents, the same iterative loop with TPU documentation injected, and a TPU-enabled Autocomp configuration augmented with the same TPU-specific documentation. Across the full 50-benchmark suite with Gemini 3 Flash, best-of-N solves 13/50 benchmarks at 1.01x geomean and iterative refinement reaches 32/50 at 1.18x. Injecting TPU documentation lifts iterative refinement to 48/50 at 1.28x and raises per-sample correctness from 5.8% to 37.3%. Autocomp solves 45/50 but converts those correct kernels into 1.36x geomean with 76% of benchmarks beating XLA. On the 8 hand-tuned references, Autocomp reaches 1.60x against the XLA baseline, showing strong performance against the 2.08x Tokamax geomean but trailing on the specialized paged and ragged attention operators. A Gemini 3.1 Pro ablation lifts Autocomp to 49.1% per-sample correctness and a 3.13x geomean. High-quality TPU kernel generation remains open, and we release the benchmark, profiling harness, and baseline results to support reproducible research.
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Cloud applications are increasingly shifting from large monolithic services to complex graphs of loosely-coupled microservices. Despite the advantages of modularity and elasticity microservices offer, they also complicate cluster management and performance debugging, as dependencies between tiers introduce backpressure and cascading QoS violations.
We present Sage, a machine learning-driven root cause analysis system for interactive cloud microservices. Sage leverages unsupervised ML models to circumvent the overhead of trace labeling, captures the impact of dependencies between microservices to determine the root cause of unpredictable performance online, and applies corrective actions to recover a cloud service’s QoS. In experiments on both dedicated local clusters and large clusters on Google Compute Engine we show that Sage consistently achieves over 93% accuracy in correctly identifying the root cause of QoS violations, and improves performance predictability.
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Autonomous Warehouse-Scale Computers
Proceedings of the 57th Annual Design Automation Conference 2020, Association for Computing Machinery, New York, NY United States
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Modern Warehouse-Scale Computers (WSCs), composed of many generations of servers and a myriad of domain specific accelerators, are becoming increasingly heterogeneous. Meanwhile, WSC workloads are also becoming incredibly diverse with different communication patterns, latency requirements, and service level objectives (SLOs). Insufficient understanding of the interactions between workload characteristics and the underlying machine architecture leads to resource over-provisioning, thereby significantly impacting the utilization of WSCs.
We present Autonomous Warehouse-Scale Computers, a new WSC design that leverages machine learning techniques and automation to improve job scheduling, resource management, and hardware-software co-optimization to address the increasing heterogeneity in WSC hardware and workloads. Our new design introduces two new layers in the WSC stack, namely: (a) a Software-Defined Server (SDS) Abstraction Layer which redefines the hardware-software boundary and provides greater control of the hardware to higher layers of the software stack through stable abstractions; and (b) a WSC Efficiency Layer which regularly monitors the resource usage of workloads on different hardware types, autonomously quantifies the performance sensitivity of workloads to key system configurations, and continuously improves scheduling decisions and hardware resource QoS policies to maximize cluster level performance. Our new WSC design has been successfully deployed across all WSCs at Google for several years now. The new WSC design improves throughput of workloads (by 7-10%, on
average), increases utilization of hardware resources (up to 2x), and reduces performance variance for critical workloads (up to 25%).
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