In response to innovations in machine learning (ML) models, production workloads changed
radically and rapidly. TPU v4 is the fifth Google domain specific architecture (DSA) and its …

The growth of artificial intelligence applications demands ever larger and more complex
deep learning models, dominating today's—and tomorrow's—data center and high …

The massive growth of machine learning-based applications and the end of Moore's law
have created a pressing need to redesign computing platforms. We propose Lightning, the …

Silicon photonics holds significant promise in revolutionizing optical interconnects in data
centers and high performance computers to enable scaling into the Pb/s package escape …

With the widespread use of distributed machine learning (DML), many IT companies have
established networks dedicated to DML. Different communication architectures of DML have …

The bandwidth and latency requirements of modern datacenter applications have led
researchers to propose various topology designs using static, dynamic demand-oblivious …

We present CASSINI, a network-aware job scheduler for machine learning (ML) clusters.
CASSINI introduces a novel geometric abstraction to consider the communication pattern of …

We describe our experience developing what we believe to be the world's first large-scale
production deployments of lightwave fabrics used for both datacenter networking and …

As the scale of distributed training increases, it brings huge communication overhead in
clusters. Some works try to reduce the communication cost through gradient compression or …

This paper argues that fair-sharing, the holy grail of congestion control algorithms for
decades, is not necessarily a desirable property in Machine Learning (ML) training clusters …