Jun Feng Liu is an IBM Platform Computing Architect, focusing on Big data platform design and implementation. He has assisted in successfully delivering solutions to several key customers.
GPUs have been increasingly used in a broad area of applications, such as machine learning, image processing and risk analytics to achieve higher performance and lower costs (energy footprints). On the other hand, Spark has become a very popular distributed application framework for data processing and complex analytics. This talk will share GPU and Spark usage through the real world examples in cognitive computing and risk analytics in financial services. The synergy between GPU and Spark will be further explained. Finally, it will discuss the challenges in using GPU in production Spark applications and the enhancements to manage and utilize both CPU and GPU resources. Specifically, it will address - Support Spark MLlib on GPU - GPU specific resource group is introduced for facilitate mixed GPU/CPU resource management, e.g. identify the GPU sensitive stages and tasks from Spark DAG Scheduler - Enable GPU Python and Scala API for end user to declare computing logic that user wants to run on GPU devices, including shuffle configuration on transferring memory data between GPU and CPU - Enable GPU and CPU mixed resource scheduling in Spark to get better resource utilization. - Build the slot based and the multi-dimensional based solution for mixed scheduling to maximize resource usage. - Fail back capability, if the GPU fail to finish the job, it should be able to seamless back to CPU workload. - Speeding up capability, try GPU resource in the long tail case.Additional Reading: