Modern real-world application scenarios like Internet services consist of a diversity of AI and
non-AI modules with huge code sizes and long and complicated execution paths, which …

Cloud service providers improve resource utilization by co-locating latency-critical (LC)
workloads with best-effort batch (BE) jobs in datacenters. However, they usually treat multi …

Deep learning frameworks are powerful tools to support model training. They dispatch
operators by mapping them into a series of kernel functions and launching these kernel …

Different placement or collaboration policies in handling datasets and workloads across
cloud, edge, and user-end may substantially affect a cloud-edge computing environment's …

Data center networking is the central infrastructure of the modern information society.
However, benchmarking them is very challenging as the real-world network traffic is difficult …

To better design AI processors, it is critical to characterize artificial intelligence (AI)
workloads and contrast them to normal personal computer (PC) workloads. In this work, we …

GPUs play a central and indispensable role as accelerators in modern high-performance
computing (HPC) platforms, enabling a wide range of tasks to be performed efficiently …

Reproducibility and replicability (R&R) are important for research. Many communities are
beginning efforts to reward, incentivize, and highlight projects as a motive to adopt R&R …

The global community faces many pressing and uncertain challenges like pandemics and
global climate change. Information technology (IT) infrastructure has become the enabler to …

Benchmarking and evaluating deep learning models and systems necessitate a meticulous
approach to ensure comprehensive assessment. In practical applications, it is paramount to …