An FPGA-based object detector with dynamic workload balancing

Task offloading strategies based on workload balancing in ultra-dense networks

Proceedings of the Conference on Research in Adaptive and Convergent Systems - RACS '19 ◽

10.1145/3338840.3355665 ◽

2019 ◽

Author(s):

Cissoko Moussa ◽

Haibo Zhang ◽

Hasan Shamim ◽

Kaijian Liu

Keyword(s):

Dense Networks ◽

Workload Balancing ◽

Task Offloading

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GPrimer: a fast GPU-based pipeline for primer design for qPCR experiments

BMC Bioinformatics ◽

10.1186/s12859-021-04133-4 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Jeongmin Bae ◽

Hajin Jeon ◽

Min-Soo Kim

Keyword(s):

Data Structures ◽

Primer Design ◽

Main Memory ◽

Design Tools ◽

Workload Balancing ◽

Qpcr Analysis ◽

Computational Speed ◽

Entire Sequence ◽

Target Sequences ◽

Coalesced Memory

Abstract Background Design of valid high-quality primers is essential for qPCR experiments. MRPrimer is a powerful pipeline based on MapReduce that combines both primer design for target sequences and homology tests on off-target sequences. It takes an entire sequence DB as input and returns all feasible and valid primer pairs existing in the DB. Due to the effectiveness of primers designed by MRPrimer in qPCR analysis, it has been widely used for developing many online design tools and building primer databases. However, the computational speed of MRPrimer is too slow to deal with the sizes of sequence DBs growing exponentially and thus must be improved. Results We develop a fast GPU-based pipeline for primer design (GPrimer) that takes the same input and returns the same output with MRPrimer. MRPrimer consists of a total of seven MapReduce steps, among which two steps are very time-consuming. GPrimer significantly improves the speed of those two steps by exploiting the computational power of GPUs. In particular, it designs data structures for coalesced memory access in GPU and workload balancing among GPU threads and copies the data structures between main memory and GPU memory in a streaming fashion. For human RefSeq DB, GPrimer achieves a speedup of 57 times for the entire steps and a speedup of 557 times for the most time-consuming step using a single machine of 4 GPUs, compared with MRPrimer running on a cluster of six machines. Conclusions We propose a GPU-based pipeline for primer design that takes an entire sequence DB as input and returns all feasible and valid primer pairs existing in the DB at once without an additional step using BLAST-like tools. The software is available at https://github.com/qhtjrmin/GPrimer.git.

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