SIMD Libraries in C++
Explore SIMD libraries in C++, including std::simd for C++26. Learn about application-level libraries, developer libraries, and design trade-offs for exploiting SIMD in modern C++ development.
BLADE - The Allen Telescope Array CUDA-Accelerated Real-Time DSP Library
Explore GPU-accelerated real-time DSP for radio astronomy, focusing on BLADE software's innovative techniques for processing massive data streams from the Allen Telescope Array.
What is Low Latency C++? - Part 1
Explore low latency C++ across industries, examining challenges and techniques for efficient programming in time-critical systems like finance, audio, and gaming.
Python Concurrency - From Beginner to Pro
Comprehensive guide to Python concurrency, covering processes, threads, modules, race conditions, and the GIL. Includes coding examples and activities to build a multithreaded and multiprocessing web server.
GPU-Accelerated Data Analytics in Python
Explore GPU-accelerated data analytics using RAPIDS libraries in Python. Learn ETL, machine learning, graph analytics, and more with familiar APIs for faster computation and model iteration.
Practical Deep Learning for Data Scientists
Hands-on PyTorch and GPU Deep Learning tutorial for data scientists. Build common architectures, evaluate performance, and leverage compute resources effectively. Ideal for those with ML basics seeking practical DL experience.
Leveraging a Custom CPython Data Model for High Performance
Explore high-performance microprocessor design using a custom CPython data model for efficient data analysis, visualization, and problem-solving in integrated circuit development.
Python Meets Heterogeneous Computing - An Exploration of Distributed Computations
Exploring distributed computations in Python across diverse hardware, addressing challenges and strategies for optimal performance in heterogeneous computing environments.
Exploit Your GPU Power with PyCUDA and Friends
Harness GPU capabilities using PyCUDA for high-performance computing in Python, exploring techniques to accelerate data processing and scientific calculations.
High-Performance Computing on Gamer PCs
Explore high-performance computing techniques using consumer-grade hardware, optimizing algorithms for gaming PCs to achieve powerful computational results.
Python for High Performance and Scientific Computing
Explore Python's capabilities for high-performance and scientific computing, enhancing your skills in numerical simulations and data analysis for research and industry applications.
Sfrutta la potenza della GPU con PyCUDA (e compagni)
Scopri come sfruttare la potenza delle GPU per l'elaborazione parallela utilizzando PyCUDA e altre librerie Python, accelerando significativamente le prestazioni computazionali.
High-Performance Computing on Gamer PCs
Explore high-performance computing techniques using consumer-grade hardware, optimizing algorithms for gaming PCs to achieve powerful computational results.
Exploit Your GPU Power with PyCUDA - and Friends
Harness GPU power for scientific computing using PyCUDA. Learn CUDA programming model, memory spaces, and practical applications in Python for high-performance computing tasks.
Writing a Parallel and Distributed Tool for Backing Up a Multi Terabyte Data
Learn to create a parallel and distributed backup tool for handling multi-terabyte datasets, exploring efficient strategies for large-scale data management and protection.
The Cython Compiler for Python
Explore Cython, a powerful static compiler for Python, to speed up code and create fast native extensions. Learn key features, optimization techniques, and C/C++ integration.
Python's Role in Big Data Analytics - Past, Present, and Future
Explore Python's evolution in big data analytics, from its scientific computing roots to its current role in unifying HPC and big data applications, with insights on future developments.
Ganga - An Interface to the LHC Computing Grid
Explore Ganga, a Python-based interface for CERN physicists to manage and distribute data analysis tasks across global computing resources, from local machines to worldwide centers.
Performance Python for Numerical Algorithms
Explore techniques for optimizing numerical algorithms in Python, including vectorization, multi-threading, parallelization, and dynamic compiling. Practical examples using NumPy, Numba, and other libraries.
Parallelism Shootout - Threads vs Asyncio vs Multiple Processes
Explore threading, multiprocessing, and asyncio for efficient parallel data downloading. Compare mechanics and performance of each approach for handling numerous URLs.
Python Multithreading and Multiprocessing - Concurrency and Parallelism
Explore Python's threading and multiprocessing packages for concurrent and parallel programming. Learn best practices, overcome common issues, and master key concepts for efficient development.