filmov
tv
Profiling Python Workloads with Intel VTune Amplifier
Показать описание
Efficient profiling techniques can help dramatically improve the performance of code by identifying CPU and memory bottlenecks. This webinar focuses on how to profile a Python application using Intel VTune Amplifier, a full-featured profiling tool.
Paulius Velesko, an Intel Application Engineer, demonstrates how to leverage VTune's time sampling and hardware counter analysis on cases such as a pure Python application and a Python application calling C or Fortran routines (which may be either user-written or in third-party libraries).
About ALCF Many-Core Developer Sessions:
This webinar series is aimed at increasing the dialogue between ALCF users and the developers of many-core systems and software. Attendees are encouraged to bring any questions related to the ALCF's Theta system and/or the Intel Xeon Phi technology.
Paulius Velesko, an Intel Application Engineer, demonstrates how to leverage VTune's time sampling and hardware counter analysis on cases such as a pure Python application and a Python application calling C or Fortran routines (which may be either user-written or in third-party libraries).
About ALCF Many-Core Developer Sessions:
This webinar series is aimed at increasing the dialogue between ALCF users and the developers of many-core systems and software. Attendees are encouraged to bring any questions related to the ALCF's Theta system and/or the Intel Xeon Phi technology.
0:33:43
Profiling Python Workloads with Intel VTune Amplifier
0:04:03
Optimize Python* Workloads with Intel® VTune™ Profiler | Intel Software
0:54:48
Tutorial: Profile DPC++ and GPU Workload Performance-VTune, Advisor, Roofline Analysis
0:02:38
Optimize Multi-Node System Workloads with NVIDIA Nsight Systems
0:27:12
Optimization Masterclass: improving Python-based workloads in HPC
0:09:30
Lightning Talk: Profiling and Memory Debugging Tools for Distributed ML Workloads on GPUs- Aaron Shi
0:41:45
Profiling Your Application with Intel VTune and Advisor
0:41:14
Profiling DL Workloads on Supercomputing Systems ǀ Murali Krishna Emani, Argonne National Laboratory...
0:42:49
Scale EDA & ML Workloads To Clusters & Back With Dask I PyData Chicago January 2022 Meetup
0:03:49
Metrics Collector | Intel® Graphics Performance Analyzers Framework Quick Tips | Intel Software
0:51:24
Tutorial: Accelerating Deep Learning Workloads by Using Intel® AI Analytics Toolkit and 3rd Gen...
0:00:25
what it’s like to work at GOOGLE…
0:07:20
Dask in 8 Minutes: An Introduction
0:30:59
Tech talk: Characterizing Simulation and Machine Learning Workloads
0:00:56
Why I left Blizzard Entertainment
1:57:44
Intel Workshop | Accelerate AI workloads with Intel® oneDNN & oneDAL | Hack2skill
0:27:15
ATPESC 2021 8.5 Profiling DL Workloads Kyle Felker
0:01:00
Solving for Workload Optimization, sponsored by Intel and HPE (Trailer)
0:36:09
Eng & Kwon - Scaling data workloads using the best of both worlds: pandas and Spark
0:05:15
How CPUs Use Multiple Cores
0:00:19
Life at Amazon
0:30:18
[Arm DevSummit - Session] Benchmarking and Performance Analysis of ML Workloads on Arm’s Ethos U55
0:03:18
Contributing to Open Source Can Change Your Life - Here’s How to Do It
0:17:07
MLSys'2021: RL-Scope: Cross-stack Profiling for Deep Reinforcement Learning Workloads (Full Tal...
Комментарии