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SEC(R)2008
SEC(R)2008
Intel® Concurrent Collections for C++ -a model for parallel programming
Nikolay Kurtovemail: [email protected]
Software and Services Group
October 23, 2008
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Agenda
•Existing parallel programming models
•Key concepts, Blackscholes example
•Performance results
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Parallel programming is important
•Number of multi-core machines is growing
•Developers want to fully exploit architecture capabilities
But Parallel programming is hard:
•Users must reason about parallelism
•Thread synchronization
•Embedded in serial languages
−Data Overwriting
−Arbitrary Serialization
•Tuning Performance
•Depends on a platform
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Parallel Programming Models
•Improve productivity of programming
•Hide low-level details
•Provide high-level abstractions
The following models are very popular:
•OpenMP
•Cilk
•Intel® Threading Building Blocks
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OpenMP
•Perfect for Data-parallel algorithms
•Basics are easy to be applied:
#pragma omp parallel for
for (int i = 0; i < N; i++) doSomething(i);
•Advanced usage is complicated and error-prone
•Requires compiler support
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Cilk
•The programmer identifyes elements that can safely be executed in parallel
int fibonacci(int n) {
if (n < 2) return n;
int x = cilk_spawn fib(n-1);
int y = cilk_spawn fib(n-2);
cilk_sync;
return (x+y);
}
•Explicit spawning of tasks and synchronization with barriers
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Intel® Threading Building Blocks
•Implemented as a C++ library
•Requires an excellent knowledge of C++
•Provides excellent high-level abstractions
•Provides basic parallel algorithms:
−parallel_for
−parallel_sort
−parallel_while
−parallel_reduce
−parallel_do
−parallel_scan
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Existing models - summary
•The programmer explicitly expresses parallelism
•Provide an imperative algorithm description
•Many low-levels questions are solved by the programmer
•Good control over performance
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Agenda
•Existing parallel programming models
•Key concepts, Blackscholes example
•Performance results
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SEC(R)2008
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The application problem:
• Serial code
• Semantic correctness
Intel® Concurrent Collections:
• Architecture
• Actual parallelism
• Load balancing
• Distribution among processors
Ideal Parallel programming model
Domain Expert (person)
Only domain knowledge
No tuning knowledge
Tuning Expert (person, runtime, static
analysis)
No domain knowledge
Only tuning knowledge
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How people think about their application
What are high level operations?
What are the chunks of data?
What are the producer/consumer relationships?
What are the inputs and outputs?
Parameters ResultSolve
Blackscholes
A data-parallel application
Solves an equation independently for each parameters set
Blackscholes
A data-parallel application
Solves an equation independently for each parameters set
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•Step – a single high-level operation
•Item – a single data element
•Tag – an identifier of a step or an item
•Inputs/Outputs – items or tags produced or consumed by the environment
Intel® Concurrent Collections Key Concepts
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// Declarations<SolveTags: int n>;[OptionData* Parameters: int n];[float Result: int n];
// Step prescription<SolveTags> :: (Solve);
// Step execution[Parameters] -> (Solve) -> [Result];
// Input from the environment: // initialize all tags and dataenv -> <SolveTags>, [Parameters];
// Output to the environment[Result] -> env;
Textual Graph Representation
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Graph definition Translator
•Translates a graph definition into a declaration of a class
•A generated class contains properly named item collections, tag collections and step collections
•Generates a coding hints file – a template for steps definition
•Checks correctness of a graph
class blackscholes_graph_t : public Graph_t {
public:
ItemCollection_t<OptionData*> Parameters;
ItemCollection_t<float> Result;
TagCollection_t SolveTags;
StepCollection_t SolveStepCollection;
...
};
class blackscholes_graph_t : public Graph_t {
public:
ItemCollection_t<OptionData*> Parameters;
ItemCollection_t<float> Result;
TagCollection_t SolveTags;
StepCollection_t SolveStepCollection;
...
};
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Items identifiers
• Items are stored in a graph in an item collection
• Put stores an item, associates it with a tag
• Get accesses items by a tag
• Items are immutable
Tags
Steps identifiers
• Steps are prescribed by tags
• Put stores a tag, instantiates prescribed steps
• The same tag is passed to each instantiated step
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Specifying Computation
1. StepReturnValue_t Solve(
2. Blackscholes_graph_t& graph,
3. const Tag_t& step_tag)
4. {
5. OptionData* data =
6. graph.Parameters.Get(step_tag);
7. float result = solveEquation(data);
8. graph.Result.Put(step_tag, result);
9. return CNC_Success;
10.}
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Using the graph in your C++ application1. Blackscholes_graph_t my_graph;
2. for (int i = 0; i < N; i++) {3. my_graph.SolveTags.Put(Tag_t(i));4. my_graph.Parameters.Put(Tag_t(i), data[i]);5. }
6. my_graph.run();
7. for (int i = 0; i < N; i++) {8. float result = my_graph.Result.Get(Tag_t(i));9. std::cout << result << std::endl;10.}
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Steps Rescheduling
A step may begin execution before its input items are available
It will be rescheduled and started again from the beginning when the corresponding item is added to the collection
Image : k
ImageTag : k
Block : i, j
BlockTag : i,j
Result : i, jSplit : k Process : i, j
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Constraints required by the application
1.Steps have no side-effects
2.Steps call Gets before any Puts
3.Steps call Gets before allocating any memory
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Benefits from using Intel® Concurrent Collections•Improves programming productivity
−Only serial code
−No knowledge of parallel technologies required
−Determinism
−Race-free
•Portability
•Scalability
•Expert-tuning system
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Summary: How to write an application using Intel® Concurrent Collections?
1. Draw the algorithm on a chalkboard
2. Define Data structures
3. Represent the algorithm in the textual notation
4. Implement high-level operations in C++
5. Instantiate a Graph and run it
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Agenda
•Existing parallel programming models
•Key concepts, Blackscholes example
•Performance results
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Blackscholes benchmark
• Calculations for a single set of parameters are less than 500 CPU instructions
• Steps should be grouped to reduce the overhead and improve cache locality
• Automatic grain selection is an area for future research
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System Threads
Block size 200
Block Size 10
No blocks
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Dedup benchmark
•Algorithm is a pipeline
•The last pipeline stage is serial
•Feature “Steps Priorities” makes Dedup run 1.4 times faster
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Intel® ConcurrentCollectionsPthreads
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Possible model improvements
• Memory management
• Garbage collection
• Automatic grain selection
• Streaming data input
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Getting More Information
Intel® Concurrent Collections for C/C++
on WhatIf.intel.com:
http://software.intel.com/en-us/articles/
intel-concurrent-collections-for-cc
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Questions & Answers
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Thank you!
