From fba5e329ede2ffd04df21399233be14c3e061998 Mon Sep 17 00:00:00 2001 From: Stephen Chou Date: Wed, 14 Dec 2022 01:04:53 -0800 Subject: [PATCH] Fixed slides link --- _data/publications.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_data/publications.yml b/_data/publications.yml index b57b292..6ac53c1 100644 --- a/_data/publications.yml +++ b/_data/publications.yml @@ -5,7 +5,7 @@ venue: OOPSLA 2022 is_thesis: false paper_link: files/chou-oopsla22-taco-dynamic.pdf - slide_link: files/amarasinghe-oopsla22-slides.pdf + slide_link: files/amarasinghe-oopsla22-slides.pptx abstract: > Many applications, from social network graph analytics to control flow analysis, compute on sparse data that evolves over the course of program execution. Such data can be represented as dynamic sparse tensors and efficiently stored in formats (data layouts) that utilize pointer-based data structures like block linked lists, binary search trees, B-trees, and C-trees among others. These specialized formats support fast in-place modification and are thus better suited than traditional, array-based data structures like CSR for storing dynamic sparse tensors. However, different dynamic sparse tensor formats have distinct benefits and drawbacks, and performing different computations on tensors that are stored in different formats can require vastly dissimilar code that are not straightforward to correctly implement and optimize.