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Study mode:
on
1
Intro
2
The challenge
3
Watch, learn, adopt, experiment
4
Mechanical sympathy
5
Temperature as integer
6
Memory mapped files
7
Getting unsafe
8
SWAR
9
Stringless
10
Branchless programming
11
Parse the temperature
12
Keeping track
13
Which JVM?
14
Graal native-image
15
Summary
16
Results
17
Outro
Description:
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore an in-depth presentation on optimizing Java performance for parsing 1 billion rows of weather data. Dive into advanced techniques including parallelism, memory-mapped files, SWAR (SIMD Within A Register), bit twiddling, branchless code, mechanical sympathy, and Graal native compilation. Learn how to dramatically improve processing speed from over 4 minutes to under 2 seconds using various optimization strategies. Discover the power of Java's performance capabilities through practical examples and code changes, including unconventional approaches like using sun.misc.Unsafe. Gain insights into JVM selection, Graal native image compilation, and their impact on execution speed. Follow the speaker's journey of experimentation and optimization, uncovering valuable lessons in high-performance Java programming along the way.

Java Parsing Optimization: Processing 1 Billion Rows of Weather Data

GOTO Conferences
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