Data movement between the CPU and main memory is a first-order obstacle against improv
ing performance, scalability, and energy efficiency in modern systems. Computer systems …

As transistor scaling becomes increasingly more difficult to achieve, scaling the core count
on a single GPU chip has also become extremely challenging. As the volume of data to …

Recent work has shown that building GPUs with hundreds of SMs in a single monolithic chip
will not be practical due to slowing growth in transistor density, low chip yields, and …

The deep learning revolution has been enabled in large part by GPUs, and more recently
accelerators, which make it possible to carry out computationally demanding training and …

With the advancement of processor packaging technology and the looming end of Moore's
law, multi-chip-module (MCM) GPUs become a promising architecture to continue the …

Garbage collection (GC) is a standard feature for high productivity programming, saving a
programmer from many nasty memory-related bugs. However, these productivity benefits …

Exploiting data locality in GPGPUs is critical for efficiently using the smaller data caches and
handling the memory bottleneck problem. This paper proposes a thread block-centric …

Chiplets are transforming computer system designs, allowing system designers to combine
heterogeneous computing resources at unprecedented scales. Breaking larger, mono-lithic …

Multi-Chip Module (MCM) designs have emerged as a key technique to scale up a GPU's
compute capabilities in the face of slowing transistor technology. However, the …

GPUs have become indispensable accelerators for many data-intensive applications such
as scientific workloads, deep learning models, and graph analytics; these applications share …