© 2013 IBM Corporation Implement high-level parallel API in JDK Richard Ning – Enterprise Developer 1 st June 2013

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© 2013 IBM Corporation 3 Introduction to the speaker Developing enterprise application software since 1999 (C++, Java) Recent work focus: IBM JDK development My contact information: –mail:

© 2013 IBM Corporation What should you get from this talk? By the end of this session, you should be able to: –Understand implementation of high-level parallel API in JDK –Understand how parallel computing works on multi-cores

© 2013 IBM Corporation Agenda Introduction: multi-threading, multi-cores, parallel computing Case study Other high-level parallel API Roadmap 4 4

© 2013 IBM Corporation Introduction Multi-Threading Multi-core computer Parallel computing

© 2013 IBM Corporation Case study Execute the same task for every element in a loop Use multi-threading for the execution

© 2013 IBM Corporation Can it improve performance?

© 2013 IBM Corporation time CPUCPU t1 t2 t1 t2 t1 Multi-threading on computer with one core

© 2013 IBM Corporation 100% CPU usage with single thread and multi-threading Performance even decreases with extra threading consuming Can't improve performance It is useless to use multi- threading(par allel) API)

© 2013 IBM Corporation CPU1 Multi-threading on computer with multi-core

© 2013 IBM Corporation Cor4 t4 t2 t3 t1 Cor3 Cor2 Cor1 Thread runs separately on every core time

© 2013 IBM Corporation Raw thread Any improvement? Executor –Users need to create and manage it Disadvantages – Not flexible – the number of threads is hard to configure flexibly > core number, resources are consumed in thread context, even decrease performance < core number, some cores are wasted No balance, the calculation can't be allocated into every core equally – Not flexible – the number of threads is hard to configure flexibly > core number, resources are consumed in thread context, even decrease performance < core number, some cores are wasted No balance, the calculation can't be allocated into every core equally

© 2013 IBM Corporation Separate creation and execution of thread Use thread pool to reuse thread

© 2013 IBM Corporation A high-level API concurrent_for

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The API is easy to use, users only need to input executed task and data range and don't care about how they are executed. However they still have disadvantages. 1.The number of thread in thread pool isn't aligned to core number 2.Task executes an entry once, which isn't sufficient 3.A task is targeted to a thread, which isn't flexible

© 2013 IBM Corporation 123 n Thread Pool 1 3 n 2 Tasks m CPU Core Thread Task Core: 4 Thread: n Task: m Overloading : n>>4 Not flexible: m >n

© 2013 IBM Corporation Thread Pool CPU Core Thread Thread number = core number Core number doesn't align to thread number: Use fixed thread pool

© 2013 IBM Corporation Task division: another task division strategy ForkJoinPool Fork Join Task1 Task2Task3 Task5Task6Task7 Divide and conquer 1. Divide big task into small tasks recursively 2. Execute the same operation for every task 3. Join result of every small task Task4

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Better use for divide and conquer problem Previous issues (thread oversubscription and starvation, unbalancing) still exist Task dividing strategy is from users, isn't configured properly according to running condition

© 2013 IBM Corporation New parallel API based on task scheduler

© 2013 IBM Corporation Thread Pool CPU Core Thread TASKQUEUETASKQUEUE Initial status Tasks are allocated equally, One thread by one core Every thread maintains its task queue which consists of affiliated tasks

© 2013 IBM Corporation Thread Pool CPU Core Thread TASKQUEUETASKQUEUE 1015 Unbalancing loading

© 2013 IBM Corporation Thread Pool 1234 CPU Core Thread TASKQUEUETASKQUEUE Balancing loading by task stealing and adding new tasks

© 2013 IBM Corporation Parallel API with new working mechanism - concurrent_for Range: the range of data set [0, n) Strategy: the strategy of dividing range: automatic, static with granularity Task: the task which executes the same operation on range

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Other high-level parallel API Can add data set while executing it concurrently. concurrent _while Use divide_join based task to return calculation result. concurrent _reduce Sort data set concurrently. concurrent sort for example, a matrix multiply another matrix int[5][10] matrix1, int[10][5] matrix2 int[5][5] matrix3 = matrix1 * matrix2 int[5][5] matrix3 = concurrent_multiply(matrix1, matrix2) Math calculation

© 2013 IBM Corporation Anyway we always can achieve performance improvement by parallel computing based on multi-cores.

© 2013 IBM Corporation Scalable Roadmap Implement high-level parallel API in JDK based on new task scheduler Correct Portable High performance

© 2013 IBM Corporation Review of Objectives Now that youve completed this session, you are able to: –Understand design of new parallel API based on task. –Understand what parallel computing is and what is good for

© 2013 IBM Corporation Q & A

© 2013 IBM Corporation Thanks!