Phani Pendurthi
Mastercard, Principal Software Engineer
Union, Missouri, United States
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I’m a Principal Software Engineer at Mastercard with 18 years of experience across software engineering, data analytics, distributed and large-scale systems. I’ve built and optimized software architectures globally in payments, banking, telecom and manufacturing, worked across multiple countries, and mentored engineers and organizations. I explore AI, Payments, HPC, and emerging technologies to create systems that are scalable, high-performance, efficient, reliable and importantly secure systems.
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CPU, GPU, and TPU Co-Scheduling: Architectural Tradeoffs for HPC Performance, Energy, and Cost
AI workloads are exploding, and running CPUs, GPUs, and TPUs together efficiently is now critical for performance, energy, and cost in HPC systems.
In this talk, I’ll share my experience with co-scheduling MIMD CPUs, SIMT GPUs, and systolic-array TPUs, showing how differences in execution models, memory hierarchies (NUMA, HBM, on-chip SRAM), and programming abstractions shape workload partitioning, data movement, and scheduling granularity. I’ll cover static, semi-static, and dynamic strategies and highlight their impact on performance portability, energy efficiency, and cost.
Instead of isolated benchmarks, I focus on end-to-end system behavior, emphasizing performance portability, energy-aware scheduling, and cost-efficient use of open HPC software stacks effectively. You will leave with actionable insights to co-schedule heterogeneous workloads smarter, CPUs, GPUs, and TPUs effectively, unlocking higher performance, lower energy use, and cost savings for both AI and HPC workloads.
Phani Pendurthi
Mastercard, Principal Software Engineer
Union, Missouri, United States
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