Useful links¶
Document  | 
Description  | 
|---|---|
Documentation for programming NumPy-like codes on data parallel devices  | 
|
Documentation for programming Numba codes on data parallel devices the same way as you program Numba on CPU  | 
|
Documentation how to manage data and devices, how to interchange data between different tensor implementations, and how to write data parallel extensions  | 
|
Performance profiler supporting analysis of bottlenecks from function leve down to low level instructions. Supports Python and Numba  | 
|
Analyzes native and Python codes and provides an advice for better composition of heterogeneous algorithms  | 
|
Standard for writing portable Numpy-like codes targeting different hardware vendors and frameworks operating with tensor data  | 
|
Standard for writing C++-like codes for heterogeneous computing  | 
|
Free e-book how to program data parallel devices using Data Parallel C++  | 
|
OpenCl* Standard for heterogeneous programming  | 
|
Standard for floating-point arithmetic, essential for writing robust numerical codes  | 
|
Documentation for Numpy - foundational CPU library for array programming. Used in conjunction with Data Parallel Extension for Numpy*.  | 
|
Documentation for Numba - Just-In-Time compiler for Numpy-like codes. Used in conjunction with Data Parallel Extension for Numba*.  |