1. Compiling Dynamic Data Structures in Python to Enable the Use of Multi-core and Many-core Libraries
Bin Ren, Gagan Agrawal
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2. Outline Background Analysis of Python Efficiency
Design of the Framework
Linearization
Insertion
Homo-Decision
Experiments Evaluation
Conclusion
Related Work
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3. Background Programming landscape Productivity Performance
Traditional single-core programming model & programming languages:
C/C++;
Java;
C#;
Python; …
Multi-core and Many-core programming model & programming languages:
OpenMP;
MPI;
CUDA;
OpenCL; …
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4. Existing Ways to Improve Productivity
High level parallel programming model/libraries
Map-reduce/FREERIDE
CUBLAS/Tensor Contractor …
High level parallel programming languages
Chapel, X10
Pig Latin, Sawzall
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5. Our Focus Start with an existing language: (Pure) Python
Popular across a lot of communities
Easy learning curve
Very high productivity
Low performance
Utilize the existing libraries
More domain oriented
Low level languages for multi-core/ many-core environments
Optimized libraries – high performance
Relatively Low productivity
Start with an existing popular programming language
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6. Outline Background Analysis of Python Efficiency
Design of the Framework
Linearization
Insertion
Homo-Decision
Experiments Evaluation
Conclusion
Related Work
9/18/2018

7. Motivating Application
DGEMM (Double General Matrix Multiplication)
Python: Nested lists
Gen C++: Nested vector-based containers
Manual C: 1/multi-dimensional arrays
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8. Possible factors to decrease the efficiency
Interpreted programming language
Dynamic type inference
Dynamic pointer based data structures:
- list
- dictionary
- set
- …
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9. Nested List Data Structure
data[l]:
data[0]
data[1] …
data[l-1]
b1[0]
b1[1]
b1[n-1] b2
a1[0]
a1[1]
a1[m-1] a2
a1’[0]
a1’[1]
a1’[m-1]
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10. Analysis of Python Efficiency
Possible approach to improve the performance
Compiling Python to a low level programming language
Handling the typing issues during the compilation
Flattening the dynamic data structure to dense memory buffers
Challenges:
- Flattening nested dynamic data structures is not trivial
- How to reduce the overhead incurred by the flattening operations
- Only the homogeneous data can be stored into the continuous array
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11. Outline Background Analysis of Python Efficiency
Design of the Framework
Linearization
Insertion
Homo-Decision
Experiments Evaluation
Conclusion
Related Work
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12. Overview of the framework
Data Transformation;
Transformation Insertion;
Homo-Decision.
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13. Contributions Linearization Algorithm Two-stage Insertion algorithm
Transform the data set from pointer-intensive to dense memory buffer
Two-stage Insertion algorithm
A lightweight demand-driven IPRE to reduce the data transformation overhead
Homo-Decision algorithm
Whether the elements in a data set are in the same type
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14. Data Transformation Algorithms
data[l]:
data[0]
data[1] …
data[l-1]
b1[0]
b1[1]
b1[n-1] b2
a1[0]
a1[1]
a1[m-1] a2
a1’[0]
a1’[1]
a1’[m-1]
Linearizing Alg
Mapping Alg
Linear_data[ ]:
a1’[0]
a1’[m-1] … a2 b2 m n l
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15. The Insertion Algorithm
Two-stage algorithm:
Insert the linearization function immediately before every usage of the dynamic data structure
Optimize the generated code by an inter-procedural partial redundancy elimination (IPRE) algorithm
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16. The Insertion Algorithm
Basic PRE algorithm
Eliminate Duplicated Evaluations of Exps
Eliminate Duplicated Function Calls of Linear
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17. Design of the framework
IPRE algorithm
main
kmeans_reduction
kmeans
update_clusters
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18. IPRE Overview Simpler PRE algorithm Demand-driven Light Weight
Developed by Paleri et al.
Demand-driven Light Weight
Analyze on a small number of procedures
Assumption
Inter-procedure pointer-analysis and alias-analysis
Pull-out Strategy
If there is no modification to list li between the entry and linearize(li) in some procedure, we can pull it outside the procedure
Propagate Strategy
The modification of list li in some procedure to its parent procedure
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19. IPRE
points
clusters
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20. The Data Flow Analysis to Check Homogeneity
Primitive way
Check the type of the elements in the data set one by one
Shortcoming: time consuming
Our method
Start from the program itself
Check the possibility of assigning the data with different types
Simple data flow analysis – high efficiency
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21. Example myList = [ ] loop header if (someCon) myList.append (a)
myList.append (b) …
… = myList
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22. Outline Background Analysis of Python Efficiency
Design of the Framework
Linearization
Insertion
Homo-Decision
Experiments Evaluation
Conclusion
Related Work
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23. Experiment setup Environment Code versions
K-means and PCA (FREERIDE): Glenn System from OSC
DGEMM and TM (CUBLAS & Tensor Contractor Generated CUDA code): Bale System from OSC
Code versions
Python: Python code
Gen C++: Shedskin generated C++ code
WOPRE: Generated code without IPRE
WPRE: Generated code with IPRE
OPT: Generated code with further optimization
Manual: The manual C code
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24. K-Means 800M; k = 100; iter = 1 <left> & iter = 10 <right>
By IPRE, the overhead can be reduced, especially in the multi-iteration version;
The performance of the final version is very similar to the manual version, and overhead is around 10%.
For smaller data set (8M), Python needs sec and sec
For the same data set (800M), Gen C++ needs sec and sec
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25. PCA
800M; row = 1000; column = 100, 000
For smaller data set (8M), Python runs sec
For the same data set (800M), Gen C++ needs 3280 sec
IPRE has to be applied;
The performance of the final version is very similar to the manual version, and the overhead is around 10%~20%.
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26. Linear Algebra Applications
DGEMM (left) & Tensor Multiplication (right)
The performance of DGEMM for 1000*1000 has been reported in former section
The performance of TM: for conf. 1, python runs sec and Gen C++ runs sec
The IPRE can reduce the overhead by around 50%
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27. Outline Background Analysis of Python Efficiency
Design of the Framework
Linearization
Insertion
Homo-Decision
Experiments Evaluation
Conclusion
Related Work
9/18/2018

28. Conclusion Propose a compilation framework
Compile pure Python to invoke multi-core and many-core libraries
Design three novel algorithms
Inter-procedural PRE algorithm, Homogeneity Checking algorithm and Linearization-Mapping schema
Good experiments performance
Generated code only 10%-20% slower than the hand-written C
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29. Related Work Improve the efficiency of Python
Using extension libraries
- NumPy & SciPy
- PyCUDA & PyOpenCL
- Copperhead …
Compiling to low level programming language
- Cython
- Pyrex
- Shedskin …
Improve the efficiency of Pointer-based data structure
Explore the nature of irregular algorithms
Automatic pool allocation
Transformation of recursive data/computation structures
Data reordering
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30. Thank you for your attention!
Any Questions?
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