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 …

Building large AI fleets to support the rapidly growing DL workloads is an active research
topic for modern cloud providers. Generating accurate benchmarks plays an essential role in …

Today's Internet Services are undergoing fundamental changes and shifting to an intelligent
computing era where AI is widely employed to augment services. In this context, many …

Earlier-stage evaluations of a new AI architecture/system need affordable AI benchmarks.
Only using a few AI component benchmarks like MLPerf alone in the other stages may lead …

Most AI projects start with a Python notebook running on a single laptop; however, one
usually needs to go through a mountain of pains to scale it to handle larger dataset (for both …

Finding the right heuristics to optimize code has always been a difficult and mostly manual
task for compiler engineers. Today this task is near-impossible as hardware-software …

Benchmarking and co-design are essential for driving optimizations and innovation around
ML models, ML software, and next-generation hardware. Full workload benchmarks, eg …

Modern high performance computing clusters heavily rely on accelerators to overcome the
limited compute power of CPUs. These supercomputers run various applications from …

As Moore's Law has slowed and Dennard Scaling has ended, architects are increasingly
turning to heterogeneous parallelism and domain-specific hardware-software co-designs …

In this article, we present JUWELS Booster, a recently commissioned high-performance
computing system at the Jülich Supercomputing Center. With its system architecture, most …