title: GPUTreeShap: massively parallel exact calculation of SHAP scores for tree ensembles

authors: Mitchell, Rory

subjects: GPU computing

abstract: SHapley Additive exPlanation (SHAP) values (Lundberg & Lee, 2017) provide a game theoretic interpretation of the predictions of machine learning models based on Shapley values (Shapley, 1953). While exact calculation of SHAP values is computationally intractable in general, a recursive polynomial-time algorithm called TreeShap (Lundberg et al., 2020) is available for decision tree models. However, despite its polynomial time complexity, TreeShap can become a significant bottleneck in practical machine learning pipelines when applied to large decision tree ensembles. Unfortunately, the complicated TreeShap algorithm is difficult to map to hardware accelerators such as GPUs. In this work, we present GPUTreeShap, a reformulated TreeShap algorithm suitable for massively parallel computation on graphics processing units. Our approach first preprocesses each decision tree to isolate variable sized sub-problems from the original recursive algorithm, then solves a bin packing problem, and finally maps sub-problems to single-instruction, multiple-thread (SIMT) tasks for parallel execution with specialised hardware instructions. With a single NVIDIA Tesla V100-32 GPU, we achieve speedups of up to 19× for SHAP values, and speedups of up to 340× for SHAP interaction values, over a state-of-the-art multi-core CPU implementation executed on two 20-core Xeon E5-2698 v4 2.2 GHz CPUs. We also experiment with multi-GPU computing using eight V100 GPUs, demonstrating throughput of 1.2 M rows per second—equivalent CPU-based performance is estimated to require 6850 CPU cores.

text: GPUTreeShap: massively parallel exact calculation of SHAP scores for tree ensembles SHapley Additive exPlanation (SHAP) values (Lundberg & Lee, 2017) provide a game theoretic interpretation of the predictions of machine learning models based on Shapley values (Shapley, 1953). While exact calculation of SHAP values is computationally intractable in general, a recursive polynomial-time algorithm called TreeShap (Lundberg et al., 2020) is available for decision tree models. However, despite its polynomial time complexity, TreeShap can become a significant bottleneck in practical machine learning pipelines when applied to large decision tree ensembles. Unfortunately, the complicated TreeShap algorithm is difficult to map to hardware accelerators such as GPUs. In this work, we present GPUTreeShap, a reformulated TreeShap algorithm suitable for massively parallel computation on graphics processing units. Our approach first preprocesses each decision tree to isolate variable sized sub-problems from the original recursive algorithm, then solves a bin packing problem, and finally maps sub-problems to single-instruction, multiple-thread (SIMT) tasks for parallel execution with specialised hardware instructions. With a single NVIDIA Tesla V100-32 GPU, we achieve speedups of up to 19× for SHAP values, and speedups of up to 340× for SHAP interaction values, over a state-of-the-art multi-core CPU implementation executed on two 20-core Xeon E5-2698 v4 2.2 GHz CPUs. We also experiment with multi-GPU computing using eight V100 GPUs, demonstrating throughput of 1.2 M rows per second—equivalent CPU-based performance is estimated to require 6850 CPU cores.

year: 2022

title: Finding video on the web

authors: Cunningham, Sally Jo

subjects: design

abstract: At present very little is known about how people locate and view videos. This study draws a rich picture of everyday video seeking strategies and video information needs, based on an ethnographic study of New Zealand university students. These insights into the participants’ activities and motivations suggest potentially useful facilities for a video digital library.

text: Finding video on the web At present very little is known about how people locate and view videos. This study draws a rich picture of everyday video seeking strategies and video information needs, based on an ethnographic study of New Zealand university students. These insights into the participants’ activities and motivations suggest potentially useful facilities for a video digital library.

year: 2008-02

title: Editorial: Special Issue: Ignored no longer: Emerging Indigenous researchers on Indigenous psychologies

authors: Groot, Shiloh Ann Maree

abstract: In this special issue, edited by Mohi, Bridgette, Shiloh, Pat and Darren, we showcase work conducted within several such Indigenous psychologies. This collection of papers from emerging Indigenous scholars reflect a vibrant, healthy and supportive research environment in which conversations relevant to Indigenous peoples are taking place, and where culturally diverse perspectives and methods are valued and accepted. Here, culture is not simply seen as an abstract set of concepts. Culture constitutes a field of human action, meaning making, and self-production. It is through culture that all people construct themselves and make sense of the world (Groot, Hodgetts, Nikora, & Leggat-Cook, 2011; Nikora, Rua, & Te Awekōtuku, 2007).

text: Editorial: Special Issue: Ignored no longer: Emerging Indigenous researchers on Indigenous psychologies In this special issue, edited by Mohi, Bridgette, Shiloh, Pat and Darren, we showcase work conducted within several such Indigenous psychologies. This collection of papers from emerging Indigenous scholars reflect a vibrant, healthy and supportive research environment in which conversations relevant to Indigenous peoples are taking place, and where culturally diverse perspectives and methods are valued and accepted. Here, culture is not simply seen as an abstract set of concepts. Culture constitutes a field of human action, meaning making, and self-production. It is through culture that all people construct themselves and make sense of the world (Groot, Hodgetts, Nikora, & Leggat-Cook, 2011; Nikora, Rua, & Te Awekōtuku, 2007).

year: 2012