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Intro

What is "data parallel" computing?

Running example: sum of C+A+B

Can a coder dream of electric chips ?

An ordinary load-store machine

working assumption: data = arrays

Load-store machine, with arrays

How the Java VM virtualizes a CPU

Old school multi-threading

Threads can step on each others' toes

What went wrong?

In search of the right notation

Timing can be everything

Partition the data, not the code

Partitioned data is naturally simple

Split the data, keep one code stream

What could go wrong?

Communication dominates eventually

Let's stripe the data across the CPUs

Options for localizing data (2)

What is a Java "GPU thread"? (2)

Issue: Placed data needs to be aligned

Mesh computing, with private memory

Regarding vectorization

Summary: Java liabilities / challenges

Summary: Java assets

Description:

Explore data parallel computing in this 51-minute Java conference talk featuring John Rose. Learn about the concept of data parallel programming through a running example of summing C+A+B. Discover how the Java Virtual Machine virtualizes a CPU and the challenges of old school multi-threading. Examine the importance of timing and the benefits of partitioning data instead of code. Investigate options for localizing data, the concept of Java "GPU threads," and the challenges of placed data alignment. Delve into mesh computing with private memory and vectorization. Conclude with a summary of Java's liabilities, challenges, and assets in the context of data parallel programming.

Data Parallel Programming - Concepts and Challenges in Java

Java

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#Programming #Programming Languages #Java #Computer Science #Memory Management #High Performance Computing #Parallel Computing #Parallel Algorithms #Concurrent Programming #Multi-Threading #GPU Computing

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