Serverless functions provide elastic scaling and a fine-grained billing model, making
Function-as-a-Service (FaaS) an attractive programming model. However, for distributed …

We present a highly scalable, parallel implementation of first-principles electron dynamics
coupled with molecular dynamics (MD). By using optimized kernels, network topology aware …

With the continuous rise in complexity of modern supercomputers, optimizing the
performance of large-scale parallel programs is becoming increasingly challenging …

Network congestion is one of the primary causes of performance degradation, performance
variability and poor scaling in communication-heavy parallel applications. However, the …

Training and fine-tuning large language models (LLMs) with hundreds of billions to trillions
of parameters requires tens of thousands of GPUs, and a highly scalable software stack. In …

Task map** on torus networks has traditionally focused on either reducing the maximum
dilation or average number of hops per byte for messages in an application. These metrics …

Algebraic multigrid (AMG) is often viewed as a scalable O (n) solver for sparse linear
systems. Yet, AMG lacks parallel scalability due to increasingly large costs associated with …

Advancement in high-performance computing allows us to calculate properties of
increasingly complex materials with unprecedented accuracy. At the same time, to take full …

The era of extremely heterogeneous supercomputing brings with itself the devil of increased
performance variation and reduced reproducibility. There is a lack of understanding in the …

Interference between jobs competing for network bandwidth on a fat-tree cluster can cause
significant variability and degradation in performance. These performance issues can be …