SKU/Artículo: AMZ-B0FXWV8572
High-Performance Programming with Julia: A Hands-on Guide to Leveraging Parallelism, Concurrency, and Distributed Computing for Maximum Speed
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Kindle
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0.76 kg
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Amazon
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USA
Sobre este producto
- You've been there, haven't you? You write a brilliant piece of code—a simulation, a data analysis script, a complex model. It works perfectly on your small test data. Then you try it on the real dataset, and reality hits. It's going to take hours... maybe days. You face the dreaded choice: accept the slowness, or embark on a painful, time-consuming rewrite in another, faster language, risking bugs and doubling your workload.What if there was a better way? What if the language you used for prototyping could also deliver production speed? That's the promise of Julia, and this book is your map to turning that promise into reality. We'll show you how Julia "thinks" and how you can write code that helps the compiler unleash its full potential, taking you from frustrating waits to lightning-fast results.What's Inside: Your Hands-On JourneyThis isn't just theory; it's a practical workshop. Get ready to code, measure, and optimize!Foundations: Understand what performance means (latency vs. throughput), master Julia's benchmarking and profiling tools, and grasp the critical concept of type stability.Blazing-Fast Serial Code: Learn the secrets to optimizing single-threaded code before you parallelize, focusing on eliminating memory allocations and leveraging multiple dispatch.The Three Pillars: Get crystal-clear definitions and analogies for Parallelism, Concurrency, and Distributed Computing, so you always choose the right tool for your specific bottleneck.Hands-On Parallelism: Dive into using multiple threads (@threads, @spawn) to crush CPU-bound tasks, complete with a Mandelbrot set case study. Learn to avoid data races with Locks and Atomics.Hands-On Concurrency: Master Tasks, async, await, and Channels to build efficient I/O-bound applications, demonstrated with a concurrent web scraper. Learn robust error handling.Hands-On Distributed Computing: Scale beyond one machine using Distributed.jl. Learn addprocs, @everywhere, remotecall, pmap, and @distributed with a Monte Carlo simulation case study.GPU Acceleration: Get started with CUDA.jl, understand the CPU vs. GPU architecture, move data with CuArray, and write your first GPU kernels (both the easy broadcasting way and the advanced custom way). Learn the crucial overhead costs.Capstone Project: Put it all together by building a hybrid, distributed log file analyzer from a slow baseline to a massively accelerated final version.Who It's Meant ForThis book is for you if:You're a scientist, engineer, data analyst, quantitative researcher, or programmer hitting performance walls in languages like Python, R, or MATLAB.You're already using Julia but feel your code isn't as fast as it could be.You want to learn the principles of high-performance computing (HPC) in a modern, productive language.You have some programming experience (you know loops and functions) but don't need to be a computer science expert.Unlock the Speed You Need. Today.Stop waiting for your code. Stop the painful rewrites. Learn to harness the full power of Julia and your hardware.High-Performance Programming with Julia provides the knowledge, the tools, and the hands-on practice you need to transform your slow programs into high-speed computational engines.Grab your copy now and start writing faster Julia code today!
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