English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Future computing systems, from handhelds to supercomputers, will undoubtedlybe more parallel and heterogeneous than todays systems to provide moreperformance and energy efficiency. Thus, GPUs are increasingly being used toaccelerate general purpose applications, including applications with datadependent, irregular control flow and memory access patterns. However, thegrowing complexity, exposed memory hierarchy, incoherence, heterogeneity, andparallelism will make accelerator based systems progressively more difficult toprogram. In the foreseeable future, the vast majority of programmers will nolonger be able to extract additional performance or energy savings from nextgeneration systems be-cause the programming will be too difficult. Automaticperformance analysis and optimization recommendation tools have the potentialto avert this situation. They embody expert knowledge and make it available tosoftware developers when needed. In this paper, we describe and evaluate such atool.

A Tool for Automatically Suggesting Source-Code Optimizations for Complex GPU Kernels