HPC, Big Data & Data Science
Developing software tools for accelerated and differentiable scientific computing using JAX
<p><a href="https://docs.jax.dev/en/latest/">JAX</a> is an open-source Python package for high-performance numerical computing. It provides a familiar NumPy style interface but with the advantages of allowing computations to be dispatched to accelerator devices such as graphics and tensor processing units, and supporting transformations to automatically differentiate, vectorize and just-in-time compile functions. While extensively used in machine learning applications, JAX's design also makes it ideal for scientific computing tasks such as simulating numerical models and fitting them to data.</p>
<p>This lightning talk will introduce JAX's interface and computation model, and some of its key function transformations. I will also briefly introduce the <a href="https://data-apis.org/array-api/latest/#">Python Array API standard</a> and explain how it can be used to write portable code which works across JAX, NumPy and other array backends.</p>
Weitere Infos
| Live Stream | https://live.fosdem.org/watch/h1308 |
|---|---|
| Format | devroom |
| Sprache | Englisch |
Weitere Sessions
| 01.02.26 |
<p>Scientific models are today limited by compute resources, forcing approximations driven by feasibility rather than theory. They consequently miss important physical processes and decision-relevant regional details. Advances in AI-driven supercomputing — specialized tensor accelerators, AI compiler stacks, and novel distributed systems — offer unprecedented computational power. Yet, scientific applications such as ocean models, often written in Fortran, C++, or Julia and built for ...
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| 01.02.26 |
<p>ROCm™ has been AMD’s software foundation for both high-performance computing (HPC) and AI workloads and continues to support the distinct needs of each domain. As these domains increasingly converge, ROCm™ is evolving into a more modular and flexible platform. Soon, the distribution model shifts to a core SDK with domain-specific add-ons—such as HPC—allowing users to select only the components they need. This reduces unnecessary overhead while maintaining a cohesive and ...
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| 01.02.26 |
<p>Wherever research software is developed and used, it needs to be installed, tested in various ways, benchmarked, and set up within complex workflows. Typically, in order to perform such tasks, either individual solutions are implemented - imposing significant restrictions due to the lack of portability - or the necessary steps are performed manually by developers or users, a time-consuming process, highly susceptible to errors. Furthermore, particularly in the field of high-performance ...
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| 01.02.26 |
<h2>Content</h2> <p>High-frequency wave simulations in 3D (with e.g. Finite Elements) involve systems with hundreds of millions unknowns (up to 600M in our runs), prompting the use of massively parallel algorithms. In the harmonic regime, we favor Domain Decomposition Methods (DDMs) where local problems are solved in smaller regions (subdomains) and the full solution of the PDE is recovered iteratively. This requires each rank to own a portion of the mesh and to have a view on neighboring ...
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| 01.02.26 |
<p>As the computing needs of the world have grown, the need for parallel systems has grown to match. However, the programming languages used to target those systems have not had the same growth. General parallel programming targeting distributed CPUs and GPUs is frequently locked behind low-level and unfriendly programming languages and frameworks. Programmers must choose between parallel performance with low-level programming or productivity with high-level languages.</p> <p><a ...
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| 01.02.26 |
<p>With the rapid acceleration of ML/AI research in the last couple of years, the already energy-hungry HPC platforms have become even more demanding. A major part of this energy consumption is due to users’ workloads and it is only by the participation of end users that it is possible to reduce the overall energy consumption of the platforms. However, most of the HPC platforms do not provide any sort of metrics related to energy consumption, nor the performance metrics out of the box, which ...
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| 01.02.26 |
<p><a href="www.ecmwf.int">ECMWF</a> manages petabytes of meteorological data critical for weather and climate research. But traditional storage formats pose challenges for machine learning, big-data analytics, and on-demand workflows. </p> <p>We propose a solution which introduces a Zarr store implementation for creating virtual views of ECMWF’s Fields Database (FDB), enabling users to access GRIB data as if it were a native Zarr dataset. Unlike existing approaches such as VirtualiZarr or ...
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