Explore the core parallelism strategies—task, data, pipeline, and model parallelism—and learn how to choose the right pattern for your production workload, with concrete examples from Spark, Ray, Dask, and Kubernetes.
Table of Contents Introduction What Is Dispenso? Why Choose Dispenso Over Other Thread Pools? Core Concepts and Architecture 4.1 Task Representation 4.2 Worker Threads and Queues 4.3 Work Stealing Mechanics Getting Started: Building and Integrating Dispenso Basic Usage Patterns 6.1 Submitting Simple Tasks 6.2 Futures and Continuations 6.3 Parallel Loops with parallel_for Advanced Techniques 7.1 Task Dependencies with when_all and when_any 7.2 Custom Allocators and Memory Management 7.3 Thread‑Local Storage & Affinity 7.4 Integrating with Existing Codebases (e.g., OpenCV, Eigen) Performance Benchmarking 8.1 Micro‑benchmarks: Overhead vs. Raw Threads 8.2 Real‑World Scenario: Image Processing Pipeline Best Practices and Common Pitfalls Conclusion Resources Introduction Parallel programming in modern C++ has evolved dramatically since the introduction of the <thread> library in C++11. While the standard library provides low‑level primitives, most production‑grade applications need higher‑level abstractions that can efficiently schedule work across many cores, handle task dependencies, and minimize overhead. This is where Dispenso shines. ...
Introduction In modern software development, improving application responsiveness and performance is critical. Two fundamental concepts that help achieve this are parallelism and asynchronous programming. Although often used interchangeably, they represent distinct approaches to handling multiple tasks and can be combined for maximum efficiency. This tutorial provides an in-depth exploration of parallelism and asynchronous programming: what they mean, how they differ, how they relate to concurrency, and how to implement them effectively. We will also include practical examples and point to valuable resources for further learning. ...
Python is one of the most popular programming languages today, widely used for everything from web development to data science. In an era of multi-core processors and big data, a crucial skill that elevates your Python programming to the next level is mastering parallelism—the art of running multiple computations simultaneously to speed up processing and utilize modern hardware efficiently. This comprehensive tutorial will guide you from the basics of parallel computing in Python to advanced techniques, complete with practical examples, performance considerations, and valuable resources to explore further. ...