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

Dedicated accelerator hardware has become essential for processing AI-basedworkloads, leading to the rise of novel accelerator architectures. Furthermore,fundamental differences in memory architecture and parallelism have made theseaccelerators targets for scientific computing.  The sequence alignment problem is fundamental in bioinformatics; we haveimplemented the $X$-Drop algorithm, a heuristic method for pairwise alignmentthat reduces search space, on the Graphcore Intelligence Processor Unit (IPU)accelerator. The $X$-Drop algorithm has an irregular computational pattern,which makes it difficult to accelerate due to load balancing.  Here, we introduce a graph-based partitioning and queue-based batch system toimprove load balancing. Our implementation achieves $10\times$ speedup over astate-of-the-art GPU implementation and up to $4.65\times$ compared to CPU. Inaddition, we introduce a memory-restricted $X$-Drop algorithm that reducesmemory footprint by $55\times$ and efficiently uses the IPU's limitedlow-latency SRAM. This optimization further improves the strong scalingperformance by $3.6\times$.

Space Efficient Sequence Alignment for SRAM-Based Computing: X-Drop on the Graphcore IPU