Rabab Alomairy
I am a postdoctoral researcher at the MIT JuliaLab and an HPC enthusiast who loves solving complex problems by thinking in parallel. My research intersects High-Performance Computing (HPC) and Artificial Intelligence (AI), exploring how advanced computational techniques can optimize AI algorithms for increased efficiency and effectiveness. I was honored as one of the Rising Stars in Computational and Data Sciences by U.S. Department of Energy. My collaborations extend internationally, including with the Innovative Computing Lab at the University of Tennessee and MINES ParisTech. In Summer 2021, I was a visiting scholar at the Innovative Computing Lab, where I contributed to a milestone of the Software for Linear Algebra Targeting Exascale (SLATE) project , a joint initiative of the U.S. Department of Energy’s Office of Science and the National Nuclear Security Administration (NNSA).
Sessions
Julia offers the best of both worlds: high-level expressiveness combined with low-level performance, allowing developers to leverage modern hardware accelerators without needing expertise in hardware-specific languages. This workshop demonstrates how Julia makes high-performance computing (HPC) accessible by covering key topics such as resource configuration, distributed computing, CPU and GPU code optimization, and scalable workflows.
Julia offers the best of both worlds: high-level expressiveness combined with low-level performance, allowing developers to leverage modern hardware accelerators without needing expertise in hardware-specific languages. This workshop demonstrates how Julia makes high-performance computing (HPC) accessible by covering key topics such as resource configuration, distributed computing, CPU and GPU code optimization, and scalable workflows.
Modern hardware like NVIDIA’s H100, GB100, and AMD’s MI300 accelerators demand flexible, high-performance software. DLA.jl modernizes dense linear algebra with a unified, hardware-agnostic API, while Dagger.jl enables dynamic task scheduling across CPUs and GPUs. Together, they provide scalable, efficient computation without vendor lock-in. This talk explores their impact on HPC, AI, and scientific computing, highlighting future directions in mixed precision and adaptive scheduling.
The DARPA-MIT SmartSolve project tackles the challenge of dynamically selecting optimal algorithms and architectures through an automated discovery framework. As part of this effort, we present advances on optimizing algorithm and data structure choices tailored to linear algebra. Contributions include automated benchmarking across diverse matrix patterns, database-driven selection via Pareto analysis, and exploring large language models for automatic heuristic generation.
Round-table discussion of everything about Dagger.jl. Success or failure stories, ideas for new features, discussion of existing bugs or missing documentation, and more!