JavaOne2013: Implement a High Level Parallel API - Richard Ning
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Transcript of JavaOne2013: Implement a High Level Parallel API - Richard Ning
© 2013 IBM Corporation
Richard Ning – Enterprise Developer
9/24/2013
Implement high-level parallel API in JDK
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© 2013 IBM Corporation2
Important Disclaimers
– THE INFORMATION CONTAINED IN THIS PRESENTATION IS PROVIDED FOR INFORMATIONAL PURPOSES ONLY.
– WHILST EFFORTS WERE MADE TO VERIFY THE COMPLETENESS AND ACCURACY OF THE INFORMATION CONTAINED IN THIS PRESENTATION, IT IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED.
– ALL PERFORMANCE DATA INCLUDED IN THIS PRESENTATION HAVE BEEN GATHERED IN A CONTROLLED ENVIRONMENT. YOUR OWN TEST RESULTS MAY VARY BASED ON HARDWARE, SOFTWARE OR INFRASTRUCTURE DIFFERENCES.
– ALL DATA INCLUDED IN THIS PRESENTATION ARE MEANT TO BE USED ONLY AS A GUIDE.
– IN ADDITION, THE INFORMATION CONTAINED IN THIS PRESENTATION IS BASED ON IBM’S CURRENT PRODUCT PLANS AND STRATEGY, WHICH ARE SUBJECT TO CHANGE BY IBM, WITHOUT NOTICE.
– IBM AND ITS AFFILIATED COMPANIES SHALL NOT BE RESPONSIBLE FOR ANY DAMAGES ARISING OUT OF THE USE OF, OR OTHERWISE RELATED TO, THIS PRESENTATION OR ANY OTHER DOCUMENTATION.
– NOTHING CONTAINED IN THIS PRESENTATION IS INTENDED TO, OR SHALL HAVE THE EFFECT OF: CREATING ANY WARRANT OR REPRESENTATION FROM IBM, ITS AFFILIATED COMPANIES OR ITS OR THEIR SUPPLIERS AND/OR LICENSORS.
© 2013 IBM Corporation
About me
Richard Ning
IBM JDK development
Developing enterprise application
software since 1999 (C++, Java)
My contact information:
–:
mail:[email protected]
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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
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Agenda
Introduction: multi-threading, multi-cores, parallel computing
Case study
Other high-level parallel API
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Roadmap4
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Introduction
Multi-Threading
Multi-core computer
Parallel computing
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Case study
■ Execute the same task for every element in a loop
■ Use multi-threading for the execution
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■ Can it improve performance?8
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CPU
t1t2
t1t2
t1
■ Multi-threading on computer with one core
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■ 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(paral
lel API)
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■ Multi-threading on computer with multi-core
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Cor4 t4
t2
t3
t1
Cor3
Cor2
Cor1
Thread runs separately on every core
time
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■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 wastedNo balance, the calculation can't be allocated into every core equally
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■ Separate creation and execution of thread■ Use thread pool to reuse thread
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■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
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1 2 3 nThread Pool
1 3 n2Tasks
m
1 2 3 4
CPUCore
Thread
Task
Core: 4Thread: nTask: m
Overloading: n>>4
Not flexible: m >n
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1 2 3 4
Thread Pool
1 2 3 4
CPUCore
Thread
Thread number = core number
Core number doesn't align to thread number: Use fixed thread pool
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Task division: another task division strategy ForkJoinPool
ForkJoin
Task2 Task3
Task5 Task6 Task7
Divide and conquer
1. Divide big task into small tasks recursively2. Execute the same operation for every task3. Join result of every small task
Task4
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Task1
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Better use for divide and conquer problem Balancing: Work queue by thread and task stealing Oversubscription and starvation: Configuring thread number
Task dividing is static instead of dynamic. Task dividing granularity isn't configured properly according to running condition.
Task daviding strategy is from programmers who need to design it themselves in different implementation scenarios.
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New parallel API based on task scheduler
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1 2 3 4
Thread Pool
1 2 3 4
CPUCore
Thread
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2
3
4
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TASKQUEUE
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Initial statusTasks are allocated equally,One thread by one coreEvery thread maintains its task
queue which consists of
affiliated tasks25
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1 2 3 4
Thread Pool
1 2 3 4
CPUCore
Thread
2
3
4
5
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Unbalancing loading
TASKQUEUE
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1 2 3 4
Thread Pool
1 2 3 4
CPUCore
Thread
2
3 22
10
4
15
5
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Balancing loading by
task stealing and
adding new tasks who
probably have different
task granularity.
TASKQUEUE
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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 fixed granularity. In
automatic case, task granularity is probably different
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.concurrentsort
for example, a matrix multiply another matrixint[5][10] matrix1 , int[10][5] matrix2int[5][5] matrix3 = matrix1 * matrix2int[5][5] matrix3 = concurrent_multiply(matrix1, matrix2)
Math calculation
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Anyway we always can achieve performance improvement by
parallel computing based on multi-cores.
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Scalable
Roadmap
■Implement high-level parallel API in JDK based on new task scheduler
Correct
Portable
High performance
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Review of Objectives
■Now that you’ve 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
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Q & A
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Thanks!
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