Datacenter-scale AI training clusters consisting of thousands of domain-specific accelerators
(DSA) are used to train increasingly-complex deep learning models. These clusters rely on a …

The variety and complexity of microservices in warehouse-scale data centers has grown
precipitously over the last few years to support a growing user base and an evolving product …

Performance optimization for large-scale applications has recently become more important
as computation continues to move towards data centers. Data-center applications are …

Far memory, where memory accesses are non-local, has become more popular in recent
years as a solution to expand memory size and avoid memory stranding. Prior far memory …

Large Language Models (LLMs) have demonstrated remarkable performance on assisting
humans in programming and facilitating programming automation. However, existing …

The large instruction working sets of private and public cloud workloads lead to frequent
instruction cache misses and costs in the millions of dollars. While prior work has identified …

Prefetching is a well-studied technique for addressing the memory access stall time of
contemporary microprocessors. However, despite a large body of related work, the memory …

SmartNICs are on the rise as a packet processing platform, with the trend towards a uniform
P4 programming model. However, unleashing SmartNIC packet processing performance in …

Memory allocation represents significant compute cost at the warehouse scale and its
optimization can yield considerable cost savings. One classical approach is to increase the …

Modern data center applications have rapidly expanding instruction footprints that lead to
frequent instruction cache misses, increasing cost and degrading data center performance …