Details

Autor(en) / Beteiligte

• Cadenelli, Nicola
• Jaks̆ić, Zoran
• Polo, Jordà
• Carrera, David

Titel

Considerations in using OpenCL on GPUs and FPGAs for throughput-oriented genomics workloads

Ist Teil von

• Future generation computer systems, 2019-05, Vol.94, p.148-159

Ort / Verlag

Elsevier B.V

Erscheinungsjahr

2019

Quelle

Access via ScienceDirect (Elsevier)

Beschreibungen/Notizen

• The recent upsurge in the available amount of health data and the advances in next-generation sequencing are setting the ground for the long-awaited precision medicine. To process this deluge of data, bioinformatics workloads are becoming more complex and more computationally demanding. For this reasons they have been extended to support different computing architectures, such as GPUs and FPGAs, to leverage the form of parallelism typical of each of such architectures. The paper describes how a genomic workload such as k-mer frequency counting that takes advantage of a GPU can be offloaded to one or even more FPGAs. Moreover, it performs a comprehensive analysis of the FPGA acceleration comparing its performance to a non-accelerated configuration and when using a GPU. Lastly, the paper focuses on how, when using accelerators with a throughput-oriented workload, one should also take into consideration both kernel execution time and how well each accelerator board overlaps kernels and PCIe transferred. Results show that acceleration with two FPGAs can improve both time- and energy-to-solution for the entire accelerated part by a factor of 1.32x. Per contra, acceleration with one GPU delivers an improvement of 1.77x in time-to-solution but of a lower 1.49x in energy-to-solution due to persistently higher power consumption. The paper also evaluates how future FPGA boards with components (i.e., off-chip memory and PCIe) on par with those of the GPU board could provide an energy-efficient alternative to GPUs. •Refactoring of OpenCL GPU code to efficiently run on multiple FPGAs.•Multi-kernel FPGA design for k-mer generation that saturates on-board DRAM bandwidth.•Time, energy, and power evaluation of GPU and FPGAs offloading.•Analysis of how accelerators parts (i.e., off-chip memory and PCIe) can hinder performance.•Estimation of how next FPGA boards constitute a real asset for more energy-efficient genomics workloads.