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

The sizes of GPU applications are rapidly growing. They are exhausting thecompute and memory resources of a single GPU, and are demanding the move tomultiple GPUs. However, the performance of these applications scalessub-linearly with GPU count because of the overhead of data movement acrossmultiple GPUs. Moreover, a lack of hardware support for coherency exacerbatesthe problem because a programmer must either replicate the data across GPUs orfetch the remote data using high-overhead off-chip links. To address theseproblems, we propose a multi-GPU system with truly shared memory (MGPU-TSM),where the main memory is physically shared across all the GPUs. We eliminateremote accesses and avoid data replication using an MGPU-TSM system, whichsimplifies the memory hierarchy. Our preliminary analysis shows that MGPU-TSMwith 4 GPUs performs, on average, 3.9x? better than the current best performingmulti-GPU configuration for standard application benchmarks.

MGPU-TSM: A Multi-GPU System with Truly Shared Memory