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UID:087d273d-05c8-442e-b831-47eb72715c08@support.access-ci.org
DTSTAMP:20250606T115702Z
DTSTART:20250909T180000Z
DTEND:20250909T193000Z
SUMMARY:Python for HPC
DESCRIPTION:SummaryIn this session, we will explore two transformative Pyth
 on technologies—Numba and Dask—that empower researchers to bridge the 
 gap between Python’s flexibility and the performance demands of supercom
 puting environments. These tools unlock new possibilities for accelerating
  computationally intensive tasks and scaling workflows across clusters.Ses
 sion Outline:Supercharging Python with Numba: Just-in-Time (JIT) Compilati
 onLearn how Numba dynamically compiles performance-critical Python functio
 ns into optimized machine code, bypassing Python’s interpreter overhead.
  We’ll demonstrate how a few simple decorators can accelerate numerical 
 and scientific code to near C/Fortran speeds while maintaining Python’s 
 readability and interactivity.Parallelism in Python: Threads, Processes, a
 nd the Global Interpreter Lock (GIL)Dive into Python’s concurrency model
 , including the challenges posed by the GIL for multi-threaded programs. D
 iscover how Numba’s nogil mode enables true multi-threading for CPU-boun
 d tasks and how Dask leverages multi-processing to parallelize workflows a
 cross all available cores on a single node.Distributed Computing with Dask
 : Scaling Beyond a Single MachineExtend your computations to multi-node HP
 C clusters using Dask’s distributed arrays and data frames. We’ll show
 case how Dask seamlessly scales familiar NumPy and pandas workflows to han
 dle datasets larger than memory or across thousands of cores, all while ma
 naging task scheduling, load balancing, and fault toleranceWho Should Atte
 nd?This tutorial is designed for scientists, engineers, and developers wor
 king with computational workloads. Familiarity with Python basics is helpf
 ul but not required—attendees will leave with practical skills to optimi
 ze and scale Python code in HPC environments.InstructorDr. Andrea Zonca ha
 s a background in Cosmology; he has been working on analyzing Cosmic Micro
 wave Background data from the Planck Satellite. At SDSC, he leads a group 
 of high-performance and AI Computing experts helping scientists port their
  data analysis pipelines to national supercomputers. Andrea has been devel
 oping in Python since 2004 and maintains the open-source software projects
  healpy and PySM3 for the Cosmic Microwave Background community. He regula
 rly blogs about science and computing at [zonca.dev](https://zonca.dev).Se
 e a full list of SDSC's upcoming training and events here.
URL:https://support.access-ci.org/events/8043
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