Power and energy efficiency are now critical concerns in extreme-scale high-performance
scientific computing. Many extreme-scale computing systems today (for example: Top500) …

Emerging edge computing platforms often contain machine learning (ML) accelerators that
can accelerate inference for a wide range of neural network (NN) models. These models are …

Byte-addressable non-volatile memory (NVM) features high density, DRAM comparable
performance, and persistence. These characteristics position NVM as a promising new tier …

Energy consumption is one of the top challenges for achieving the next generation of
supercomputing. Codesign of hardware and software is critical for improving energy …

For many decades, progress in computing hardware has been closely associated with
CMOS logic density, performance, and cost. As such, slowdown in 2-D scaling, frequency …

This paper presents a novel nearest neighbor search algorithm achieving TPU (Google
Tensor Processing Unit) peak performance, outperforming state-of-the-art GPU algorithms …

The recently introduced roofline model plots the performance of executed code against its
operational intensity (operations count divided by memory traffic). It also includes two …

We present preliminary results of the Roofline Toolkit for multicore, manycore, and
accelerated architectures. This paper focuses on the processor architecture characterization …

Reducing communication is an important objective, as it can save energy or improve the
performance of a communication-bound application. The graph algorithm PageRank …

GPU-accelerated programs are becoming increasingly common in HPC, personal
computers, and even handheld devices, making it important to optimize their energy …