DelayRepay: delayed execution for kernel fusion in Python
| Autor: | Kuba Kaszyk, John Magnus Morton, Christophe Dubach, Michael O'Boyle, Murray Cole, Michel Steuwer, Jiawen Sun, Lu Li |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0303 health sciences
Speedup Computer science NumPy 010103 numerical & computational mathematics Dynamic compilation Parallel computing Python (programming language) Data structure 01 natural sciences 03 medical and health sciences 0101 mathematics Performance improvement Lazy evaluation computer 030304 developmental biology Data transmission computer.programming_language |
| Zdroj: | DLS |
| Popis: | Python is a popular, dynamic language for data science and scientific computing. To ensure efficiency, significant numerical libraries are implemented in static native languages. However, performance suffers when switching between native and non-native code, especially if data has to be converted between native arrays and Python data structures. As GPU accelerators are increasingly used, this problem becomes particularly acute. Data and control has to be repeatedly transferred between the accelerator and the host. In this paper, we present DelayRepay, a delayed execution framework for numeric Python programs. It avoids excessive switching and data transfer by using lazy evaluation and kernel fusion. Using DelayRepay, operations on NumPy arrays are executed lazily, allowing multiple calls to accelerator kernels to be fused together dynamically. DelayRepay is available as a drop-in replacement for existing Python libraries. This approach enables significant performance improvement over the state-of-the-art and is invisible to the application programmer. We show that our approach provides a maximum 377× speedup over NumPy - a 409% increase over the state of the art. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|