![The Needs of Scientists and Engineers
ment group) or letting the users/stakeholders know how the 100
software works (open source, scientific paper publication).
Other Using Tool is ’Standard’
80
Reason given for use of programming language
Only language known
3.8 Non-functional requirements
% of respondents
Tool is Easy to Use
Required 60
The respondents were asked to rate a series of non-functional Open Source
Reason for use of tools
Favourite
requirements on the following Likert scale: 40
Performance Cost (or lack thereof) of Tool
20
1. very unimportant Legacy Project Organisation
Ease of use 0
2. unimportant Features
Reliability
Functionality
Usability
Availability
Flexibility
Performance*
Portability
Testability
Maintainability
Tracability*
Reusability
Developer experience
Version Control is ’Required’
3. neither Features
Cross-platform compatability Improve Ease of Coding
4. important
0 10 20 30 40 50 60 0 5
Very Unimportant 10 Neither 15 20
Very Important
Unimportant Number of respondents (out of 46)
Important
5. very important Number of respondents
Figure 7: Reasons for Choice of Programming Lan- Figure 18: Importance why non-functional require-
Figure 9: Reasons of tools are used
This scale was chosen so that the relative importance of
guage ments as rated by respondents
non-functional requirements could be determined from re- 60
spondents’ answers. A straight ranking of non-functional re-
quirements would only indicate how important respondents
Modelling 50
Table 1: Combined important and very important
considered each non-functional requirement in comparison
ratings for non-functional requirements
Number of respondents
Framework
to others, but would not provide any information regard- 40
Ranking Requirement Combined Important
ing how importantTracking
Bug/Change a non-functional requirement was over-
30 and Very Important
all. The neutral response of ‘neither’ was included as some
Ratings (%)
respondents may not consider a non-functional requirement
Build Tools
Tool type
20 1 Reliability 100
or are unaware of it.
Libraries/Packages 2 Functionality 95
Non-functional requirements from the Software Require-
3 Maintainability [Nguyen-Hoan et al, 2010]
90
10
ments Specification Data Item described in United States
Testing](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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[Harvard CS264] 10b - cl.oquence: High-Level Language Abstractions for Low-Level Programming (Cyrus Omar, CMU)
- 1. High-Level Language Features for Low-Level Programming Cyrus Omar Computer Science Department Carnegie Mellon University http://www.cs.cmu.edu/~comar/
- 2. The Needs of Scientists and Engineers ment group) or letting the users/stakeholders know how the 100 software works (open source, scientific paper publication). Other Using Tool is ’Standard’ 80 Reason given for use of programming language Only language known 3.8 Non-functional requirements % of respondents Tool is Easy to Use Required 60 The respondents were asked to rate a series of non-functional Open Source Reason for use of tools Favourite requirements on the following Likert scale: 40 Performance Cost (or lack thereof) of Tool 20 1. very unimportant Legacy Project Organisation Ease of use 0 2. unimportant Features Reliability Functionality Usability Availability Flexibility Performance* Portability Testability Maintainability Tracability* Reusability Developer experience Version Control is ’Required’ 3. neither Features Cross-platform compatability Improve Ease of Coding 4. important 0 10 20 30 40 50 60 0 5 Very Unimportant 10 Neither 15 20 Very Important Unimportant Number of respondents (out of 46) Important 5. very important Number of respondents Figure 7: Reasons for Choice of Programming Lan- Figure 18: Importance why non-functional require- Figure 9: Reasons of tools are used This scale was chosen so that the relative importance of guage ments as rated by respondents non-functional requirements could be determined from re- 60 spondents’ answers. A straight ranking of non-functional re- quirements would only indicate how important respondents Modelling 50 Table 1: Combined important and very important considered each non-functional requirement in comparison ratings for non-functional requirements Number of respondents Framework to others, but would not provide any information regard- 40 Ranking Requirement Combined Important ing how importantTracking Bug/Change a non-functional requirement was over- 30 and Very Important all. The neutral response of ‘neither’ was included as some Ratings (%) respondents may not consider a non-functional requirement Build Tools Tool type 20 1 Reliability 100 or are unaware of it. Libraries/Packages 2 Functionality 95 Non-functional requirements from the Software Require- 3 Maintainability [Nguyen-Hoan et al, 2010] 90 10 ments Specification Data Item described in United States Testing
- 3. The State of Scientific Programming Today C, Fortran, CUDA, OpenCL MATLAB, Python, R, Perl Fast Productive Control over memory allocation Low syntactic overhead Control over data movement Read-eval-print loop (REPL) Access to hardware primitives Flexible data structures and abstractions Portability Nice development environments Tedious Slow Type annotations, templates, pragmas Dynamic lookups and indirection abound Obtuse compilers, linkers, preprocessors Automatic memory management can cause problems No support for high-level abstractions Scientists relieve the tension by: • writing overall control flow and basic data analysis routines in a high-level language • calling into a low-level language for performance-critical sections (can be annoying)
- 4. The State of Scientific Programming Tomorrow C, Fortran, CUDA, OpenCL MATLAB, Python, R, Perl Fast Productive Control over memory allocation Low syntactic overhead Control over data movement Read-eval-print loop (REPL) Access to hardware primitives Flexible data structures and abstractions Portability Nice development environments Tedious Slow Type annotations, templates, pragmas Dynamic lookups and indirection abound Obtuse compilers, linkers, preprocessors Automatic memory management can cause problems No support for high-level abstractions Scientists relieve any remaining tension by: • writing overall control flow and basic data analysis routines in a high-level language • calling into cl.oquence for performance-critical sections (can be annoying)
- 5. What is cl.oquence? A low-level programming language that maps closely onto, and compiles down to, OpenCL. What is OpenCL? OpenCL is an emerging standard for low-level programming in heterogeneous computing environments. It is designed as a library that can be used from a variety of higher-level language. What is a heterogeneous computing environment? A heterogeneous computing environment is an environment where many different compute devices and address spaces are available. Devices can include multi-core CPUs (using a variety of instruction sets), GPUs, hybrid-core processors like the Cell BE and other specialized accelerators. Why should I use cl.oquence? • Same core type system (including pointers) and performance profile as OpenCL • Usable from any host language that has OpenCL bindings • Type inference and extension inference eliminates annotational burden • Simplified syntax is a subset of Python, can use existing tools • Structural polymorphism gives you generic programming by default • New features: • Higher-order functions • Default arguments for functions • Python as the preprocessor and module system • Rich support for compile-time metaprogramming • Write compiler extensions, new basic types as libraries; modular, clean design • Light-weight and easy to integrate into any build process • Packaged with special Python host bindings that eliminate even basic overhead when using from within Python • Built on top of pyopencl and numpy
- 6. What is cl.oquence? A low-level programming language that maps closely onto, and compiles down to, OpenCL. What is OpenCL? OpenCL is an emerging standard for low-level programming in heterogeneous computing environments. It is designed as a library that can be used from a variety of higher-level language. What is a heterogeneous computing environment? A heterogeneous computing environment is an environment where many different compute devices and address spaces are available. Devices can include multi-core CPUs (using a variety of instruction sets), GPUs, hybrid-core processors like the Cell BE and other specialized accelerators. Why should I use cl.oquence? • Same core type system (including pointers) and performance profile as OpenCL • Usable from any host language that has OpenCL bindings • Type inference and extension inference eliminates annotational burden • Simplified syntax is a subset of Python, can use existing tools • Structural polymorphism gives you generic programming by default • New features: • Higher-order functions • Default arguments for functions • Python as the preprocessor and module system • Rich support for compile-time metaprogramming • Write compiler extensions, new basic types as libraries; modular, clean design • Light-weight and easy to integrate into any build process • Packaged with special Python host bindings that eliminate even basic overhead when using from within Python • Built on top of pyopencl and numpy
- 7. What is cl.oquence? A low-level programming language that maps closely onto, and compiles down to, OpenCL. What is OpenCL? OpenCL is an emerging standard for low-level programming in heterogeneous computing environments. It is designed as a library that can be used from a variety of higher-level language. What is a heterogeneous computing environment? A heterogeneous computing environment is one where many different devices and address spaces must be managed. Examples of devices include multi-core CPUs (using a variety of instruction sets), GPUs, hybrid-core processors like the Cell BE and other specialized accelerators. Why should I use cl.oquence? • Same core type system (including pointers) and performance profile as OpenCL • Usable from any host language that has OpenCL bindings • Type inference and extension inference eliminates annotational burden • Simplified syntax is a subset of Python, can use existing tools • Structural polymorphism gives you generic programming by default • New features: • Higher-order functions • Default arguments for functions • Python as the preprocessor and module system • Rich support for compile-time metaprogramming • Write compiler extensions, new basic types as libraries; modular, clean design • Light-weight and easy to integrate into any build process • Packaged with special Python host bindings that eliminate even basic overhead when using from within Python • Built on top of pyopencl and numpy
- 8. What is cl.oquence? A low-level programming language that maps closely onto, and compiles down to, OpenCL. What is OpenCL? OpenCL is an emerging standard for low-level programming in heterogeneous computing environments. It is designed as a library that can be used from a variety of higher-level language. What is a heterogeneous computing environment? A heterogeneous computing environment is one where many different devices and address spaces must be managed. Examples of devices include multi-core CPUs (using a variety of instruction sets), GPUs, hybrid-core processors like the Cell BE and other specialized accelerators. Why should I use cl.oquence? • Same core type system (including pointers) and performance profile as OpenCL • Usable from any host language that has OpenCL bindings • Type inference and extension inference eliminates annotational burden • Simplified syntax is a subset of Python, can use existing tools • Structural polymorphism gives you generic programming by default • New features: • Higher-order functions • Default arguments for functions • Python as the preprocessor and module system • Rich support for compile-time metaprogramming • Write compiler extensions, new basic types as libraries; modular, clean design • Light-weight and easy to integrate into any build process • Packaged with special Python host bindings that eliminate even basic overhead when using from within Python • Built on top of pyopencl and numpy
- 9. OpenCL // Parallel elementwise sum __kernel void sum(__global float* a, __global float* b, __global float* dest) { size_t gid = get_global_id(0); // Get thread index dest[gid] = a[gid] + b[gid]; } __kernel void sum(__global int* a, __global int* b, __global int* dest) { size_t gid = get_global_id(0); dest[gid] = a[gid] + b[gid]; } __kernel void sum(__global short* a, __global int* b, __global int* dest) { size_t gid = get_global_id(0); dest[gid] = a[gid] + b[gid]; } __kernel void sum(__global float* a, __global double* b, __global float* dest) { #pragma ... size_t gid = get_global_id(0); dest[gid] = a[gid] + b[gid]; } ... // Parallel elementwise product __kernel void prod(__global float* a, __global float* b, __global float* dest) { size_t gid = get_global_id(0); // Get thread index dest[gid] = a[gid] * b[gid]; } __kernel void prod(__global float* a, __global float* b, __global float* dest) {
- 10. OpenCL // Parallel elementwise sum __kernel void sum_ff(__global float* a, __global float* b, __global float* dest) { size_t gid = get_global_id(0); // Get thread index dest[gid] = a[gid] + b[gid]; } __kernel void sum_ii(__global int* a, __global int* b, __global int* dest) { size_t gid = get_global_id(0); dest[gid] = a[gid] + b[gid]; } __kernel void sum(__global short* a, __global int* b, __global int* dest) { size_t gid = get_global_id(0); dest[gid] = a[gid] + b[gid]; } __kernel void sum(__global float* a, __global double* b, __global float* dest) { #pragma ... size_t gid = get_global_id(0); dest[gid] = a[gid] + b[gid]; } ... // Parallel elementwise product __kernel void prod(__global float* a, __global float* b, __global float* dest) { size_t gid = get_global_id(0); // Get thread index dest[gid] = a[gid] * b[gid]; } __kernel void prod(__global float* a, __global float* b, __global float* dest) {
- 11. OpenCL // Parallel elementwise sum __kernel void sum_ff(__global float* a, __global float* b, __global float* dest) { size_t gid = get_global_id(0); // Get thread index dest[gid] = a[gid] + b[gid]; } __kernel void sum_ii(__global int* a, __global int* b, __global int* dest) { size_t gid = get_global_id(0); dest[gid] = a[gid] + b[gid]; } __kernel void sum(__global short* a, __global int* b, __global int* dest) { size_t gid = get_global_id(0); dest[gid] = a[gid] + b[gid]; } __kernel void sum(__global float* a, __global double* b, __global float* dest) { #pragma ... size_t gid = get_global_id(0); dest[gid] = a[gid] + b[gid]; } ... // Parallel elementwise product __kernel void prod(__global float* a, __global float* b, __global float* dest) { size_t gid = get_global_id(0); // Get thread index dest[gid] = a[gid] * b[gid]; } __kernel void prod(__global float* a, __global float* b, __global float* dest) {
- 12. OpenCL // Parallel elementwise sum __kernel void sum_ff(__global float* a, __global float* b, __global float* dest) { size_t gid = get_global_id(0); // Get thread index dest[gid] = a[gid] + b[gid]; } __kernel void sum_ii(__global int* a, __global int* b, __global int* dest) { size_t gid = get_global_id(0); dest[gid] = a[gid] + b[gid]; } __kernel void sum_fi(__global float* a, __global int* b, __global float* dest) { size_t gid = get_global_id(0); dest[gid] = a[gid] + b[gid]; } __kernel void sum_di(__global double* a, __global int* b, __global double* dest) { #pragma OPENCL EXTENSION cl_khr_fp64 : enable size_t gid = get_global_id(0); dest[gid] = a[gid] + b[gid]; } ... // Parallel elementwise product __kernel void prod(__global float* a, __global float* b, __global float* dest) { size_t gid = get_global_id(0); // Get thread index dest[gid] = a[gid] * b[gid]; } __kernel void prod(__global float* a, __global float* b, __global float* dest) {
- 13. OpenCL // Parallel elementwise sum __kernel void sum_ff(__global float* a, __global float* b, __global float* dest) { size_t gid = get_global_id(0); // Get thread index dest[gid] = a[gid] + b[gid]; } __kernel void sum_ii(__global int* a, __global int* b, __global int* dest) { size_t gid = get_global_id(0); dest[gid] = a[gid] + b[gid]; } __kernel void sum_fi(__global float* a, __global int* b, __global float* dest) { size_t gid = get_global_id(0); dest[gid] = a[gid] + b[gid]; } __kernel void sum_df(__global double* a, __global int* b, __global double* dest) { #pragma OPENCL EXTENSION cl_khr_fp64 : enable size_t gid = get_global_id(0); dest[gid] = a[gid] + b[gid]; } ... // Parallel elementwise product __kernel void prod(__global float* a, __global float* b, __global float* dest) { size_t gid = get_global_id(0); // Get thread index dest[gid] = a[gid] * b[gid]; } __kernel void prod(__global float* a, __global float* b, __global float* dest) {
- 14. OpenCL // Parallel elementwise sum __kernel void sum_ff(__global float* a, __global float* b, __global float* dest) { size_t gid = get_global_id(0); // Get thread index dest[gid] = a[gid] + b[gid]; } __kernel void sum_ii(__global int* a, __global int* b, __global int* dest) { size_t gid = get_global_id(0); dest[gid] = a[gid] + b[gid]; } __kernel void sum_fi(__global float* a, __global int* b, __global float* dest) { size_t gid = get_global_id(0); dest[gid] = a[gid] + b[gid]; } __kernel void sum_df(__global double* a, __global int* b, __global double* dest) { #pragma OPENCL EXTENSION cl_khr_fp64 : enable size_t gid = get_global_id(0); dest[gid] = a[gid] + b[gid]; } ... ... // Parallel elementwise product __kernel void prod(__global float* a, __global float* b, __global float* dest) { size_t gid = get_global_id(0); // Get thread index dest[gid] = a[gid] * b[gid]; } __kernel void prod(__global float* a, __global float* b, __global float* dest) {
- 15. OpenCL // Parallel elementwise sum __kernel void sum_ff(__global float* a, __global float* b, __global float* dest) { size_t gid = get_global_id(0); // Get thread index dest[gid] = a[gid] + b[gid]; } __kernel void sum_ii(__global int* a, __global int* b, __global int* dest) { size_t gid = get_global_id(0); dest[gid] = a[gid] + b[gid]; } __kernel void sum_fi(__global float* a, __global int* b, __global float* dest) { size_t gid = get_global_id(0); dest[gid] = a[gid] + b[gid]; } __kernel void sum_df(__global double* a, __global int* b, __global double* dest) { #pragma OPENCL EXTENSION cl_khr_fp64 : enable size_t gid = get_global_id(0); dest[gid] = a[gid] + b[gid]; } ... ... // Parallel elementwise product __kernel void prod(__global float* a, __global float* b, __kernel void prod_ff(__global float* a, __global float* b, __global float* dest) { __global float* dest) { size_t gid = get_global_id(0); // Get thread index dest[gid] = a[gid] * b[gid]; } __kernel void prod(__global float* a, __global float* b, __kernel void prod_ii(__global int* a, __global int* b, __global float* dest) { __global int* dest) {
- 16. OpenCL // Parallel elementwise sum __kernel void sum_ff(__global float* a, __global float* b, __global float* dest) { size_t gid = get_global_id(0); // Get thread index dest[gid] = a[gid] + b[gid]; } __kernel void sum_ii(__global int* a, __global int* b, __global int* dest) { size_t gid = get_global_id(0); dest[gid] = a[gid] + b[gid]; } __kernel void sum_fi(__global float* a, __global int* b, __global float* dest) { size_t gid = get_global_id(0); dest[gid] = a[gid] + b[gid]; } __kernel void sum_df(__global double* a, __global int* b, __global double* dest) { #pragma OPENCL EXTENSION cl_khr_fp64 : enable size_t gid = get_global_id(0); dest[gid] = a[gid] + b[gid]; } ... ... // Parallel elementwise product __kernel void prod(__global float* a, __global float* b, __kernel void prod_ff(__global float* a, __global float* b, __global float* dest) { __global float* dest) { size_t gid = get_global_id(0); // Get thread index dest[gid] = a[gid] * b[gid]; } __kernel void prod(__global float* a, __global float* b, __kernel void prod_ii(__global int* a, __global int* b, __global float* dest) { __global int* dest) {
- 17. OpenCL // Parallel elementwise sum __kernel void sum_ff(__global float* a, __global float* b, __global float* dest) { size_t gid = get_global_id(0); // Get thread index dest[gid] = a[gid] + b[gid]; } __kernel void sum_ii(__global int* a, __global int* b, __global int* dest) { size_t gid = get_global_id(0); dest[gid] = a[gid] + b[gid]; } __kernel void sum_fi(__global float* a, __global int* b, My photographs tell stories of loss, human struggle, and personal exploration __global float* dest) { within landscapes scarred by technology and over-use… [I] strive to metaphorically size_t gid = get_global_id(0); and poetically link laborious actions, idiosyncratic rituals and strangely crude machines into tales about our modern experience. dest[gid] = a[gid] + b[gid]; } Robert ParkeHarrison __kernel void sum_df(__global double* a, __global int* b, __global double* dest) { #pragma OPENCL EXTENSION cl_khr_fp64 : enable size_t gid = get_global_id(0); dest[gid] = a[gid] + b[gid]; } ... ... // Parallel elementwise product __kernel void prod(__global float* a, __global float* b, __kernel void prod_ff(__global float* a, __global float* b, __global float* dest) { __global float* dest) { size_t gid = get_global_id(0); // Get thread index dest[gid] = a[gid] * b[gid]; } __kernel void prod(__global float* a, __global float* b, __kernel void prod_ii(__global int* a, __global int* b, __global float* dest) { __global int* dest) {
- 18. OpenCL // Parallel elementwise sum __kernel void sum_ff(__global float* a, __global float* b, __global float* dest) { size_t gid = get_global_id(0); // Get thread index dest[gid] = a[gid] + b[gid]; } @cl.oquence.fn __kernel void sum_ii(__global int* a, __global int* b, def plus(a, b): __global int* dest) { '''Adds the two operands.''' size_t gid = get_global_id(0); return a + b dest[gid] = a[gid] + b[gid]; } @cl.oquence.fn def mul(a, b): __kernel void sum_fi(__global float* a, __global int* b, '''Multiplies the two operands.''' __global float* dest) { return a * b size_t gid = get_global_id(0); dest[gid] = a[gid] + b[gid]; } __kernel void sum_df(__global double* a, __global int* b, __global double* dest) { #pragma OPENCL EXTENSION cl_khr_fp64 : enable size_t gid = get_global_id(0); dest[gid] = a[gid] + b[gid]; } ... ... // Parallel elementwise product __kernel void prod(__global float* a, __global float* b, __kernel void prod_ff(__global float* a, __global float* b, __global float* dest) { __global float* dest) { size_t gid = get_global_id(0); // Get thread index dest[gid] = a[gid] * b[gid]; } __kernel void prod(__global float* a, __global float* b, __kernel void prod_ii(__global int* a, __global int* b, __global float* dest) { __global int* dest) {
- 19. OpenCL // Parallel elementwise sum @cl.oquence.fn __kernel void sum_ff(__global float* a, __global float* b, def ew_op(a, b, dest, op): __global float* dest) { '''Parallel elementwise binary operation.''' size_t gid = get_global_id(0); // Get thread index gid = get_global_id(0) # Get thread index dest[gid] = a[gid] + b[gid]; dest[gid] = op(a[gid], b[gid]) } @cl.oquence.fn __kernel void sum_ii(__global int* a, __global int* b, def plus(a, b): __global int* dest) { '''Adds the two operands.''' size_t gid = get_global_id(0); return a + b dest[gid] = a[gid] + b[gid]; } @cl.oquence.fn def mul(a, b): __kernel void sum_fi(__global float* a, __global int* b, '''Multiplies the two operands.''' __global float* dest) { return a * b size_t gid = get_global_id(0); dest[gid] = a[gid] + b[gid]; } __kernel void sum_df(__global double* a, __global int* b, __global double* dest) { #pragma OPENCL EXTENSION cl_khr_fp64 : enable size_t gid = get_global_id(0); dest[gid] = a[gid] + b[gid]; } ... ... // Parallel elementwise product __kernel void prod(__global float* a, __global float* b, __kernel void prod_ff(__global float* a, __global float* b, __global float* dest) { __global float* dest) { size_t gid = get_global_id(0); // Get thread index dest[gid] = a[gid] * b[gid]; } __kernel void prod(__global float* a, __global float* b, __kernel void prod_ii(__global int* a, __global int* b, __global float* dest) { __global int* dest) {
- 20. OpenCL // Parallel elementwise sum @cl.oquence.fn __kernel void sum_ff(__global float* a, __global float* b, def ew_op(a, b, dest, op): __global float* dest) { '''Parallel elementwise binary operation.''' size_t gid = get_global_id(0); // Get thread index gid = get_global_id(0) # Get thread index dest[gid] = a[gid] + b[gid]; dest[gid] = op(a[gid], b[gid]) } @cl.oquence.fn __kernel void sum_ii(__global int* a, __global int* b, def plus(a, b): __global int* dest) { '''Adds the two operands.''' size_t gid = get_global_id(0); return a + b dest[gid] = a[gid] + b[gid]; } @cl.oquence.fn def mul(a, b): __kernel void sum_fi(__global float* a, __global int* b, '''Multiplies the two operands.''' __global float* dest) { return a * b size_t gid = get_global_id(0); dest[gid] = a[gid] + b[gid]; } __kernel void sum_df(__global double* a, __global int* b, __global double* dest) { #pragma OPENCL EXTENSION cl_khr_fp64 : enable size_t gid = get_global_id(0); dest[gid] = a[gid] + b[gid]; } ... ... // Parallel elementwise product __kernel void prod(__global float* a, __global float* b, __kernel void prod_ff(__global float* a, __global float* b, __global float* dest) { __global float* dest) { size_t gid = get_global_id(0); // Get thread index dest[gid] = a[gid] * b[gid]; } __kernel void prod(__global float* a, __global float* b, __kernel void prod_ii(__global int* a, __global int* b, __global float* dest) { __global int* dest) {
- 21. OpenCL // Parallel elementwise sum @cl.oquence.fn __kernel void sum_ff(__global float* a, __global float* b, def ew_op(a, b, dest, op): __global float* dest) { '''Parallel elementwise binary operation.''' size_t gid = get_global_id(0); // Get thread index gid = get_global_id(0) # Get thread index dest[gid] = a[gid] + b[gid]; dest[gid] = op(a[gid], b[gid]) } @cl.oquence.fn __kernel void sum_ii(__global int* a, __global int* b, def plus(a, b): __global int* dest) { '''Adds the two operands.''' size_t gid = get_global_id(0); return a + b dest[gid] = a[gid] + b[gid]; } @cl.oquence.fn def mul(a, b): __kernel void sum_fi(__global float* a, __global int* b, '''Multiplies the two operands.''' __global float* dest) { return a * b size_t gid = get_global_id(0); dest[gid] = a[gid] + b[gid]; } __kernel void sum_df(__global double* a, __global int* b, __global double* dest) { #pragma OPENCL EXTENSION cl_khr_fp64 : enable size_t gid = get_global_id(0); dest[gid] = a[gid] + b[gid]; } ... ... // Parallel elementwise product __kernel void prod(__global float* a, __global float* b, __kernel void prod_ff(__global float* a, __global float* b, __global float* dest) { __global float* dest) { size_t gid = get_global_id(0); // Get thread index dest[gid] = a[gid] * b[gid]; } __kernel void prod(__global float* a, __global float* b, __kernel void prod_ii(__global int* a, __global int* b, __global float* dest) { __global int* dest) {
- 22. OpenCL // Parallel elementwise sum @cl.oquence.fn __kernel void sum_ff(__global float* a, __global float* b, def ew_op(a, b, dest, op): __global float* dest) { '''Parallel elementwise binary operation.''' size_t gid = get_global_id(0); // Get thread index gid = get_global_id(0) # Get thread index dest[gid] = a[gid] + b[gid]; dest[gid] = op(a[gid], b[gid]) } @cl.oquence.fn __kernel void sum_ii(__global int* a, __global int* b, def plus(a, b): __global int* dest) { '''Adds the two operands.''' size_t gid = get_global_id(0); return a + b dest[gid] = a[gid] + b[gid]; } @cl.oquence.fn def mul(a, b): __kernel void sum_fi(__global float* a, __global int* b, '''Multiplies the two operands.''' __global float* dest) { return a * b size_t gid = get_global_id(0); dest[gid] = a[gid] + b[gid]; } __kernel void sum_df(__global double* a, __global int* b, __global double* dest) { #pragma OPENCL EXTENSION cl_khr_fp64 : enable size_t gid = get_global_id(0); dest[gid] = a[gid] + b[gid]; } ... ... // Parallel elementwise product __kernel void prod(__global float* a, __global float* b, __kernel void prod_ff(__global float* a, __global float* b, __global float* dest) { __global float* dest) { size_t gid = get_global_id(0); // Get thread index dest[gid] = a[gid] * b[gid]; } __kernel void prod(__global float* a, __global float* b, __kernel void prod_ii(__global int* a, __global int* b, __global float* dest) { __global int* dest) {
- 23. OpenCL // Parallel elementwise sum @cl.oquence.fn __kernel void sum_ff(__global float* a, __global float* b, def ew_op(a, b, dest, op): __global float* dest) { '''Parallel elementwise binary operation.''' size_t gid = get_global_id(0); // Get thread index gid = get_global_id(0) # Get thread index dest[gid] = a[gid] + b[gid]; dest[gid] = op(a[gid], b[gid]) } @cl.oquence.fn __kernel void sum_ii(__global int* a, __global int* b, def plus(a, b): __global int* dest) { '''Adds the two operands.''' size_t gid = get_global_id(0); return a + b dest[gid] = a[gid] + b[gid]; } @cl.oquence.fn def mul(a, b): __kernel void sum_fi(__global float* a, __global int* b, '''Multiplies the two operands.''' __global float* dest) { return a * b size_t gid = get_global_id(0); dest[gid] = a[gid] + b[gid]; } __kernel void sum_df(__global double* a, __global int* b, __global double* dest) { #pragma OPENCL EXTENSION cl_khr_fp64 : enable These two libraries express the same thing. size_t gid = get_global_id(0); The code will run in precisely the same amount of time. dest[gid] = a[gid] + b[gid]; } ... ... // Parallel elementwise product __kernel void prod(__global float* a, __global float* b, __kernel void prod_ff(__global float* a, __global float* b, __global float* dest) { __global float* dest) { size_t gid = get_global_id(0); // Get thread index dest[gid] = a[gid] * b[gid]; } __kernel void prod(__global float* a, __global float* b, __kernel void prod_ii(__global int* a, __global int* b, __global float* dest) { __global int* dest) {
- 24. Two invocation models @cl.oquence.fn 1. Standalone compilation to OpenCL def ew_op(a, b, dest, op): • Use any host language that has OpenCL '''Parallel elementwise binary operation.''' bindings available gid = get_global_id(0) # Get thread index •C dest[gid] = op(a[gid], b[gid]) • C++ • Fortran @cl.oquence.fn def plus(a, b): • MATLAB '''Adds the two operands.''' • Java return a + b • .NET • Ruby @cl.oquence.fn def mul(a, b): • Python '''Multiplies the two operands.''' return a * b # Programmatically specialize and assign types to # any externally callable versions you need. sum = ew_op.specialize(op=plus) prod = ew_op.specialize(op=mul) g_int_p = cl_int.global_ptr g_float_p = cl_float.global_ptr sum_ff = sum.compile(g_float_p, g_float_p, g_float_p) sum_ii = sum.compile(g_int_p, g_int_p, g_int_p)
- 25. Two invocation models @cl.oquence.fn 1. Standalone compilation to OpenCL def ew_op(a, b, dest, op): • Use any host language that has OpenCL '''Parallel elementwise binary operation.''' bindings available gid = get_global_id(0) # Get thread index •C dest[gid] = op(a[gid], b[gid]) • C++ • Fortran @cl.oquence.fn def plus(a, b): • MATLAB '''Adds the two operands.''' • Java return a + b • .NET • Ruby @cl.oquence.fn def mul(a, b): • Python '''Multiplies the two operands.''' return a * b # Programmatically specialize and assign types to clqcc hello.clq # any externally callable versions you need. sum = ew_op.specialize(op=plus) creates hello.cl: prod = ew_op.specialize(op=mul) __kernel void sum_ff(__global float* a, __global float* b, g_int_p = cl_int.global_ptr __global float* dest) { size_t gid = get_global_id(0); g_float_p = cl_float.global_ptr dest[gid] = a[gid] + b[gid]; } sum_ff = sum.compile(g_float_p, g_float_p, g_float_p) sum_ii = sum.compile(g_int_p, g_int_p, g_int_p) __kernel void sum_ii(__global int* a, __global int* b, __global int* dest) { size_t gid = get_global_id(0); dest[gid] = a[gid] + b[gid]; }
- 26. Two invocation models @cl.oquence.fn 1. Standalone compilation to OpenCL def ew_op(a, b, dest, op): 2. Integrated into a host language '''Parallel elementwise binary operation.''' • Python + pyopencl (w/extensions) + numpy gid = get_global_id(0) # Get thread index dest[gid] = op(a[gid], b[gid]) @cl.oquence.fn def plus(a, b): '''Adds the two operands.''' return a + b @cl.oquence.fn def mul(a, b): '''Multiplies the two operands.''' return a * b # allocate two random arrays that we will be adding a = numpy.random.rand(50000).astype(numpy.float32) b = numpy.random.rand(50000).astype(numpy.float32) # transfer data to device ctx = cl.ctx = cl.Context.for_device(0, 0) a_buf = ctx.to_device(a) b_buf = ctx.to_device(b) dest_buf = ctx.alloc(like=a) # invoke function (automatically specialized as needed) ew_op(a_buf, b_buf, dest_buf, plus, global_size=a.shape, local_size=(256,)).wait() # get results result = ctx.from_device(dest_buf) # check results print la.norm(c -‐ (a + b))
- 27. Two invocation models @cl.oquence.fn 1. Standalone compilation to OpenCL def ew_op(a, b, dest, op): 2. Integrated into a host language '''Parallel elementwise binary operation.''' • Python + pyopencl (w/extensions) + numpy gid = get_global_id(0) # Get thread index dest[gid] = op(a[gid], b[gid]) @cl.oquence.fn def plus(a, b): '''Adds the two operands.''' Four simple memory management functions return a + b 1. to_device: numpy array => new buffer @cl.oquence.fn 2. from_device: buffer => new numpy array def mul(a, b): 3. alloc: empty buffer '''Multiplies the two operands.''' 4. copy: copies between existing buffers or arrays return a * b # allocate two random arrays that we will be adding Buffers hold metadata (type, shape, order) so you a = numpy.random.rand(50000).astype(numpy.float32) don’t have to provide it. b = numpy.random.rand(50000).astype(numpy.float32) # transfer data to device ctx = cl.ctx = cl.Context.for_device(0, 0) a_buf = ctx.to_device(a) b_buf = ctx.to_device(b) dest_buf = ctx.alloc(like=a) # invoke function (automatically specialized as needed) ew_op(a_buf, b_buf, dest_buf, plus, global_size=a.shape, local_size=(256,)).wait() # get results result = ctx.from_device(dest_buf) # check results print la.norm(c -‐ (a + b))
- 28. Two invocation models @cl.oquence.fn 1. Standalone compilation to OpenCL def ew_op(a, b, dest, op): 2. Integrated into a host language '''Parallel elementwise binary operation.''' • Python + pyopencl (w/extensions) + numpy gid = get_global_id(0) # Get thread index dest[gid] = op(a[gid], b[gid]) @cl.oquence.fn def plus(a, b): '''Adds the two operands.''' Four simple memory management functions return a + b 1. to_device: numpy array => new buffer @cl.oquence.fn 2. from_device: buffer => new numpy array def mul(a, b): 3. alloc: empty buffer '''Multiplies the two operands.''' 4. copy: copies between existing buffers or arrays return a * b # allocate two random arrays that we will be adding Buffers hold metadata (type, shape, order) so you a = numpy.random.rand(50000).astype(numpy.float32) don’t have to provide it. b = numpy.random.rand(50000).astype(numpy.float32) # transfer data to device ctx = cl.ctx = cl.Context.for_device(0, 0) Implicit queue associated with each context. a_buf = ctx.to_device(a) b_buf = ctx.to_device(b) dest_buf = ctx.alloc(like=a) # invoke function (automatically specialized as needed) ew_op(a_buf, b_buf, dest_buf, plus, global_size=a.shape, local_size=(256,)).wait() # get results result = ctx.from_device(dest_buf) # check results print la.norm(c -‐ (a + b))
- 29. Two invocation models @cl.oquence.auto(lambda a, b, dest, op: a.shape, (256,)) 1. Standalone compilation to OpenCL @cl.oquence.fn 2. Integrated into a host language def ew_op(a, b, dest, op): '''Parallel elementwise binary operation.''' • Python + pyopencl (w/extensions) + numpy gid = get_global_id(0) # Get thread index dest[gid] = op(a[gid], b[gid]) @cl.oquence.fn def plus(a, b): '''Adds the two operands.''' Four simple memory management functions return a + b 1. to_device: numpy array => new buffer @cl.oquence.fn 2. from_device: buffer => new numpy array def mul(a, b): 3. alloc: empty buffer '''Multiplies the two operands.''' 4. copy: copies between existing buffers or arrays return a * b # allocate two random arrays that we will be adding Buffers hold metadata (type, shape, order) so you a = numpy.random.rand(50000).astype(numpy.float32) don’t have to provide it. b = numpy.random.rand(50000).astype(numpy.float32) # transfer data to device ctx = cl.ctx = cl.Context.for_device(0, 0) Implicit queue associated with each context. a_buf = ctx.to_device(a) b_buf = ctx.to_device(b) dest_buf = ctx.alloc(like=a) The auto annotation can allow you to hide the # invoke function (automatically specialized as needed) details of parallelization from the user. ew_op(a_buf, b_buf, dest_buf, plus).wait() # get results result = ctx.from_device(dest_buf) # check results print la.norm(c -‐ (a + b))
- 30. Two invocation models @cl.oquence.auto(lambda a, b, dest, op: a.shape, (256,)) 1. Standalone compilation to OpenCL @cl.oquence.fn 2. Integrated into a host language def ew_op(a, b, dest, op): '''Parallel elementwise binary operation.''' • Python + pyopencl (w/extensions) + numpy gid = get_global_id(0) # Get thread index dest[gid] = op(a[gid], b[gid]) @cl.oquence.fn def plus(a, b): '''Adds the two operands.''' Four simple memory management functions return a + b 1. to_device: numpy array => new buffer @cl.oquence.fn 2. from_device: buffer => new numpy array def mul(a, b): 3. alloc: empty buffer '''Multiplies the two operands.''' 4. copy: copies between existing buffers or arrays return a * b # allocate two random arrays that we will be adding Buffers hold metadata (type, shape, order) so you a = numpy.random.rand(50000).astype(numpy.float32) don’t have to provide it. b = numpy.random.rand(50000).astype(numpy.float32) c = numpy.empty_like(a) # create an OpenCL context Implicit queue associated with each context. ctx = cl.ctx = cl.Context.for_device(0, 0) # invoke function (automatically specialized as needed) ew_op(In(a), In(b), Out(c), plus).wait() The auto annotation can allow you to hide the details of parallelization from the user. # check results print la.norm(c -‐ (a + b)) The In, Out and InOut constructs can help automate data movement when convenient.
- 31. OpenCL // Parallel elementwise sum @cl.oquence.fn __kernel void sum_ff(__global float* a, __global float* b, def ew_op(a, b, dest, op): __global float* dest) { '''Parallel elementwise binary operation.''' size_t gid = get_global_id(0); // Get thread index gid = get_global_id(0) # Get thread index dest[gid] = a[gid] + b[gid]; dest[gid] = op(a[gid], b[gid]) } @cl.oquence.fn __kernel void sum_ii(__global int* a, __global int* b, def plus(a, b): __global int* dest) { '''Adds the two operands.''' size_t gid = get_global_id(0); return a + b dest[gid] = a[gid] + b[gid]; } @cl.oquence.fn def mul(a, b): __kernel void sum_fi(__global float* a, __global int* b, '''Multiplies the two operands.''' __global float* dest) { return a * b size_t gid = get_global_id(0); dest[gid] = a[gid] + b[gid]; } __kernel void sum_df(__global double* a, __global int* b, __global double* dest) { #pragma OPENCL EXTENSION cl_khr_fp64 : enable These two libraries express the same thing. size_t gid = get_global_id(0); The code will run in precisely the same amount of time. dest[gid] = a[gid] + b[gid]; } ... ... // Parallel elementwise product __kernel void prod(__global float* a, __global float* b, __kernel void prod_ff(__global float* a, __global float* b, __global float* dest) { __global float* dest) { size_t gid = get_global_id(0); // Get thread index dest[gid] = a[gid] * b[gid]; } __kernel void prod(__global float* a, __global float* b, __kernel void prod_ii(__global int* a, __global int* b, __global float* dest) { __global int* dest) {
- 32. OpenCL // Parallel elementwise sum @cl.oquence.fn __kernel void sum_ff(__global float* a, __global float* b, def ew_op(a, b, dest, op): __global float* dest) { '''Parallel elementwise binary operation.''' size_t gid = get_global_id(0); // Get thread index gid = get_global_id(0) # Get thread index dest[gid] = a[gid] + b[gid]; dest[gid] = op(a[gid], b[gid]) } @cl.oquence.fn __kernel void sum_ii(__global int* a, __global int* b, def plus(a, b): __global int* dest) { '''Adds the two operands.''' size_t gid = get_global_id(0); return a + b dest[gid] = a[gid] + b[gid]; } @cl.oquence.fn def mul(a, b): __kernel void sum_fi(__global float* a, __global int* b, '''Multiplies the two operands.''' __global float* dest) { return a * b size_t gid = get_global_id(0); dest[gid] = a[gid] + b[gid]; } __kernel void sum_df(__global double* a, __global int* b, How? __global double* dest) { #pragma OPENCL EXTENSION cl_khr_fp64 : enable • cl.oquence.fn code looks like Python, but no! size_t gid = get_global_id(0); dest[gid] = a[gid] + b[gid]; • Same core type system as OpenCL (C99+) } • Type inference to eliminate type annotations (not dynamic lookups) ... ... • Extension inference to eliminate pragmas • Higher-order functions (inlined at compile-time) // Parallel elementwise product __kernel void prod(__global float* a, __global float* b, __kernel void prod_ff(__global float* a, __global float* b, • Structural polymorphism __global float* dest) { __global float* dest) { • All functions are generic by default size_t gid = get_global_id(0); // Get thread index • You can call a function with any arguments dest[gid] = a[gid] * b[gid]; that support the operations it uses. } • __kernel void prod(__global float* a, __global float* b, __kernel void prod_ii(__global int* a, __global int* b, __global float* dest) { __global int* dest) {