- Must-Know: When can parallelism make your algorithms run faster? When could it make your algorithms run slower? - Apr 25, 2017.
Efficient implementation is key to achieving the benefits of parallelization, even though parallelism is a good idea when the task can be divided into sub-tasks that can be executed independent of each other without communication or shared resources.
Tags: Interview questions, Parallelism
- Parallelism in Machine Learning: GPUs, CUDA, and Practical Applications - Nov 10, 2016.
The lack of parallel processing in machine learning tasks inhibits economy of performance, yet it may very well be worth the trouble. Read on for an introductory overview to GPU-based parallelism, the CUDA framework, and some thoughts on practical implementation.
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Tags: Algorithms, CUDA, GPU, NVIDIA, Parallelism
- Introducing Dask for Parallel Programming: An Interview with Project Lead Developer - Sep 7, 2016.
Introducing Dask, a flexible parallel computing library for analytics. Learn more about this project built with interactive data science in mind in an interview with its lead developer.
Tags: Analytics, Continuum Analytics, Dask, Data Science, Distributed, Parallelism, Python, Scientific Computing
