Computational science is crucial for delivering reliable weather and climate predictions.
However, despite decades of high-performance computing experience, there is serious …

Applying differentiable programming techniques and machine learning algorithms to foreign
programs requires developers to either rewrite their code in a machine learning framework …

Python has become the de facto language for scientific computing. Programming in Python
is highly productive, mainly due to its rich science-oriented software ecosystem built around …

Stencil computations lie at the heart of many scientific and industrial applications. Stencil
algorithms pose several challenges on machines with cache based memory hierarchy, due …

Multi-level intermediate representations (IR) show great promise for lowering the design
costs for domain-specific compilers by providing a reusable, extensible, and non-opini …

The Tensor Core Unit (TCU) has been increasingly adopted on modern high performance
processors, specialized in boosting the performance of general matrix multiplication …

Numerical simulations can help solve complex problems. Most of these algorithms are
massively parallel and thus good candidates for FPGA acceleration thanks to spatial …

While parallelism remains the main source of performance, architectural implementations
and programming models change with each new hardware generation, often leading to …

Earth system models are developed with a tight coupling to target hardware, often
containing specialized code predicated on processor characteristics. This coupling stems …

Memristive devices promise an alternative approach toward non-Von Neumann
architectures, where specific computational tasks are performed within the memory devices …