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Parallel Programming with Intel® Parallel Studio

www.intel.com/go/parallel

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2Copyright © 2009, Intel Corporation. All rights reserved. *Other brands and names are the property of their respective owners


Trend

Single Core

Dual Core

Quad Core

Multi-Core Processors Change the Rules



Paradigm Shift: More Cores, Not a Faster Clock

• Power and thermal issues limit clock frequency

• Performance increases now come from parallelism

GHz Era Multi-core Era

TIME

PER

FO

RM

AN

CE

Multi-core Needs Parallel Applications



Non-parallel (serial) is old and slow.

Unnecessarybottlenecks.



Parallelism is the key to performance.

Unnecessarybottlenecks.

Parallelprogramminglets workproceed whenready.



Parallelism is the key toperformance.

Dead end.Not the future.

The futureis here.We are ready.



Targeting the „Mass Market“ of Parallelism

MAINSTREAM DEVELOPERS



Targeting the „Mass Market“ of Parallelism

MAINSTREAM DEVELOPERS



the Mainstream Developers



the Mainstream Developers



Intel® Parallel StudioMicrosoft Visual Studio plug in* for Parallelism

The Perfect Combination for Fast & Reliable Code

DESIGN

CODE & DEBUG

VERIFY

TUNE

+



• Advisor• Composer• Inspector• Amplifier

New software tools drive adoption of multi-coreFor Microsoft Visual Studio* C++ architects, developers, and software innovators creating parallel Windows* applications.



Parallel Programming development lifecycle

DESIGNGain insight on where parallelism will most benefit existing source code

CODE & DEBUGDevelop effective applications with aC/C++ compiler and comprehensive threaded libraries

VERIFYEnsure application reliability with proactive parallel memory and threading error checking

TUNEEnhance applications with easy-to-use performance analyzer and tuner



Intel® Parallel Advisor (Available in Q3/2010 )

DESIGN PHASE

• First and only threading advisor

• See where parallelism will most benefit Windows* apps

• Step-by-step threading guidance

• Make better design decisions

• Shorter learning curve for parallelism

Gain insight on where parallelism will most benefit existing source code



Intel® Parallel Advisor Workflow



Mark Insert Annotate

ANNOTATE_SITE_BEGIN(site1);for (i=0; i<N; i++) {

ANNOTATE_TASK_BEGIN(task1);func1(i);ANNOTATE_LOCK_ACQUIRE(0);glob_variable++;ANNOTATE_LOCK_RELEASE(0);func2(i);ANNOTATE_TASK_END(task1);

}ANNOTATE_SITE_END(site1);



Views of Intel® Parallel Advisor



Intel® Parallel Composer

CODE & DEBUG PHASE

• Easier, faster parallelism for Windows* apps

• C/C++ compiler and advanced threaded libraries

• Built-in parallel debugger

• Supports OpenMP*

• Save time and increase productivity

• Code Coverage

Develop effective applications with a C/C++ compiler and comprehensive threaded libraries



Intel® Threading Building Blocks - today

• Extends C++ for parallelism– Solves C++ challenges in multiple areas– Portable to any C++ compiler, processor, O.S., already ported to a wide variety!– Coordinated with Visual Studio® 2010’s PPL and Concurrency Runtime

• Open source project started by Intel- http://threadingbuildingblocks.org

• Most used abstraction for parallelism• Flattered



Concurrent Containersconcurrent_hash_map

concurrent_queueconcurrent_bounded_queue

concurrent_vector

Miscellaneoustick_count

Generic Parallel Algorithmsparallel_for(range)

parallel_reduceparallel_for_each(begin, end)

parallel_doparallel_invoke

pipelineparallel_sortparallel_scan

Task schedulertask_group

task_structured_grouptask_scheduler_init

task_scheduler_observer

Synchronization Primitivesatomic;

mutex; recursive_mutex;spin_mutex; spin_rw_mutex;

queuing_mutex; queuing_rw_mutex;null_mutex; null_rw_mutex

Memory Allocationtbb_allocator; cache_aligned_allocator; scalable_allocator; zero_allocator

Threadstbb_thread

Thread Local Storageenumerable_thread_specific

combinable

All these m

ake up TBB



Classical parallel algorithm usage example#include "tbb/blocked_range.h"#include "tbb/parallel_for.h“using namespace tbb;

class ChangeArray{int* array;public:ChangeArray (int* a): array(a) {}

void operator()( const blocked_range<int>& r ) const{for (inti=r.begin();i!=r.end();i++ ){Foo (array[i]);}}};

void ChangeArrayParallel (int* a, int n ){parallel_for (blocked_range<int>(0, n), ChangeArray(a), auto_partitioner());}

int main (){task_scheduler_init init;int A[N];

// initialize array here…ChangeArrayParallel(A, N);

return 0;}

ChangeArrayclass definesa for-loop body for parallel_for

blocked_range– TBB templaterepresenting 1D iteration space

As usual with C++ functionobjects the main work is done inside operator()

A call to a template function parallel_for<Range, Body>:with arguments Range blocked_rangeBody ChangeArray



C++0x lambda functions support

#include "tbb/blocked_range.h"#include "tbb/parallel_for.h“using namespace tbb;

void ChangeArrayParallel (int* a, int n ){parallel_for (0, n, 1,

[=](inti) {Foo (a[i]);

}/*, auto_partitioner*/);}

int main (){//task_scheduler_init init;int A[N];

// initialize array here…ChangeArrayParallel (A, N);

return 0;}

Capture variables by valuefrom surrounding scope tocompletely mimic the non-lambdaimplementation. Note that [&]could be used to capture variables by reference.

Using lambda functions implementMyBody::operator() right insidethe call to parallel_for().

parallel_for example will transform into:

auto_partitioner is used by default

explicit task_scheduler_initcreation is now optional

parallel_for has an overload that takesstart, stop and step argument andconstructs blocked_range internally



Intel® Threading Building Blocks (TBB) andMicrosoft* Visual Studio* 2010 Parallel Pattern Library

Identical semantics shared for a core set of concurrent containers and algorithm classesparallel_for(first,last,step,f)parallel_for_eachparallel_invoketask_handletask_group_statustask_groupstructured_task_groupis_current_task_group_cancellingmissing_waitconcurrent_vector*concurrent_queue*

*These are based on Intel’s implementation used by Threading Building Blocks



Built-in Parallel Debugger Extension



Intel® Parallel Inspector

VERIFY PHASE

• Find threading errors faster

• Parallel memory and threading error checking

• Rapid analysis of code

• Help ensure Windows* application reliability

• Ship apps that run error-free

Ensure application reliability with proactive parallel memory and threading error checking



Memory Errors Analysis

• Memory Leaks

• Invalid Memory Accesses

• Invalid Partial Memory Accesses

• Mismatched Memory Allocation / Deallocation

• Missing Allocations

• Uninitialized Memory Accesses

• Uninitialized Partial Memory Access



Intel® Parallel Inspector-Memory Errors



Intel® Parallel Inspector-Memory Errors



Threading Error Analysis

• Potential Threading Errors Detected

• Data Races

• Deadlock

• Potential Privacy Infringement



Intel® Parallel Inspector-Threading Errors

Data Racing



Race Conditions

• Threads “race” against each other for resources

• Execution order is assumed but cannot be guaranteed

• Storage conflict is most common

• Concurrent access of same memory location by multiple threads– At least one thread is writing



Intel® Parallel Amplifier

TUNE PHASE

• Quickly find bottlenecks

• Tune Windows* apps faster

• Optimize app performance

• Scale apps for multi-core

• Designed for parallel apps

• Performance Analysis

• Performance Scalability Analysis

• Locks & Waits Analysis

Quickly find bottlenecks and tune parallel applications for scalable multi-core performance



Where to parallel…



Thank you!

Time for Questions now.

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