Variant Calling Parallelization on Processor-in-Memory Architecture
LAVENIER, D.; Jodin, R.; Cimadomo, R.
Show abstract
In this paper, we introduce a new combination of software and hardware PIM (Process-in-Memory) architecture to accelerate the variant calling genomic process. PIM translates into bringing data intensive calculations directly where the data is: within the DRAM, enhanced with thousands of processing units. The energy consumption, in large part due to data movement, is significantly lowered at a marginal additional hardware cost. Such design allows an unprecedented level of parallelism to process billions of short reads. Experiments on real PIM devices developed by the UPMEM company show significant speed-up compared to pure software implementation. The PIM solution also compared nicely to FPGA or GPU based acceleration bringing similar to twice the processing speed but most importantly being 5 to 8 times cheaper to deploy with up to 6 times less power consumption.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- GPU Accelerated Adaptive Banded Event Alignment for Rapid Comparative Nanopore Signal Analysis 98%
- CUDASW++4.0: Ultra-fast GPU-based Smith-Waterman Protein Sequence Database Search 97%
- An FPGA-based hardware accelerator supporting sensitivesequence homology filtering with profile hidden Markovmodels 97%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.