Topic: CPUs

Katarzyna Swirydowicz uses an asynchronous solver on an AMD system to examine the practical implementation of computational linear algebra across CPUs and GPUs. The talk introduces the relevant computational ideas, programming models, and software tools, then considers how algorithmic structure interacts with hardware capabilities. This case study illustrates both the opportunities and implementation challenges of asynchronous methods for large-scale scientific computing on GPU-accelerated systems.

Zain Asgar examines running agent inference across heterogeneous hardware. The discussion covers splitting workloads between GPUs and CPUs, compilation for different targets, automated kernel optimisation and hardware-aware scheduling. It also explores networking constraints and trade-offs among numerical precision, application accuracy, performance and cost.