In the past decade, Graphics Processing Units have played an important role in the field of
high-performance computing and they still advance new fields such as IoT, autonomous …

Graph-based intermediate representations (IRs) are widely used for powerful compiler
optimizations, either interprocedurally in pure functional languages, or intraprocedurally in …

GPU implementations of HPC applications relying on finite difference methods can include
tens of kernels that are memory-bound. Kernel fusion can improve performance by reducing …

Achieving a balance between computational speed, prediction accuracy, and universal
applicability in molecular simulations has been a persistent challenge. This paper presents …

Transfer learning in natural language processing (NLP) uses increasingly large models that
tackle challenging problems. Consequently, these applications are driving the requirements …

In recent years, the heterogeneity of both commodity and supercomputers hardware has
increased sharply. Accelerators, such as GPUs or Intel Xeon Phi co-processors, are often …

We present automatic horizontal fusion, a novel optimization technique that complements
the standard kernel fusion techniques for GPU programs. Unlike the standard fusion, whose …

Distributed training of ML models generates significant network traffic when exchanging
gradients and is sensitive to packet drops and retransmission caused by congestion when …

Parallelism can be used to significantly increase the throughput of computationally
expensive algorithms. With the widespread adoption of parallel computing platforms such as …

Metaheuristics have been showing interesting results in solving hard optimization problems.
However, they become limited in terms of effectiveness and runtime for high dimensional …