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The emergence of Next Generation Sequencing (NGS) platforms has increased the throughput of genomic sequencing and in turn the amount of data that needs to be processed, requiring highly efficient computation for its analysis. In this context, modern architectures including accelerators and non-volatile memory are essential to enable the mass exploitation of these bioinformatics workloads. This paper presents a redesign of the main component of a state-of-the-art reference-free method for variant calling, SMUFIN, which has been adapted to make the most of GPUs and NVM devices. SMUFIN relies on counting the frequency of k-mers (substrings of length k) in DNA sequences, which also constitutes a well-known problem for many bioinformatics workloads, such as genome assembly. We propose techniques to improve the efficiency of k-mer counting and to scale-up workloads like SMUFIN that used to require 16 nodes of Marenostrum 3 to a single machine with a GPU and NVM drives. Results show that although the single machine is not able to improve the time to solution of 16 nodes, its CPU time is 7.5x shorter than the aggregate CPU time of the 16 nodes, with a reduction in energy consumption of 5.5x. ; This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 639595). It is also partially supported by the Ministry of Economy of Spain under contract TIN2015-65316-P and Generalitat de Catalunya under contract 2014SGR1051, by the ICREA Academia program, and by the BSC-CNS Severo Ochoa program (SEV-2015-0493). We are also grateful to SandDisk for lending the FusionIO cards and to Nvidia who donated the Tesla K40c. ; Peer Reviewed ; Postprint (author's final draft)

The emergence of Next Generation Sequencing (NGS) platforms has increased the throughput of genomic sequencing and in turn the amount of data that needs to be processed, requiring highly efficient computation for its analysis. In this context, modern architectures including accelerators and non-volatile memory are essential to enable the mass exploitation of these bioinformatics workloads. This paper presents a redesign of the main component of a state-of-the-art reference-free method for variant calling, SMUFIN, which has been adapted to make the most of GPUs and NVM devices. SMUFIN relies on counting the frequency of k-mers (substrings of length k) in DNA sequences, which also constitutes a well-known problem for many bioinformatics workloads, such as genome assembly. We propose techniques to improve the efficiency of k-mer counting and to scale-up workloads like SMUFIN that used to require 16 nodes of Marenostrum 3 to a single machine with a GPU and NVM drives. Results show that although the single machine is not able to improve the time to solution of 16 nodes, its CPU time is 7.5x shorter than the aggregate CPU time of the 16 nodes, with a reduction in energy consumption of 5.5x. ; This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 639595). It is also partially supported by the Ministry of Economy of Spain under contract TIN2015-65316-P and Generalitat de Catalunya under contract 2014SGR1051, by the ICREA Academia program, and by the BSC-CNS Severo Ochoa program (SEV-2015-0493). We are also grateful to SandDisk for lending the FusionIO cards and to Nvidia who donated the Tesla K40c. ; Peer Reviewed ; Postprint (author's final draft)

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. ; This work was supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement s No 639595); the Ministry of Economy of Spain under contract TIN2015-65316-P and Generalitat de Catalunya, Spain under contract 2014SGR1051; the ICREA, Spain Academia program; and the BSC-CNS Severo Ochoa, Spain program (SEV-2015-0493). ; Peer Reviewed ; Postprint (published version)

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. ; This work was supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement s No 639595); the Ministry of Economy of Spain under contract TIN2015-65316-P and Generalitat de Catalunya, Spain under contract 2014SGR1051; the ICREA, Spain Academia program; and the BSC-CNS Severo Ochoa, Spain program (SEV-2015-0493). ; Peer Reviewed ; Postprint (published version)

Analysis of a patient's genomics data is the first step toward precision medicine. Such analyses are performed on expensive enterprise-class server machines because input data sets are large, and the intermediate data structures are even larger (TB-size) and require random accesses. We present a general method to perform a specific genomics problem, mutation detection, on a cheap commodity personal computer (PC) with a small amount of DRAM. We construct and access large histograms of k-mers efficiently on external storage (SSDs) and apply our technique to a state-of-the-art referencefree genomics algorithm, SMUFIN, to create SMUFIN-F. We show that on two PCs, SMUFIN-F can achieve the same throughput at only one third (36%) the hardware cost and half (45%) the energy compared to SMUFIN on an enterprise-class server. To the best of our knowledge, SMUFIN-F is the first reference-free system that can detect somatic mutations on commodity PCs for whole human genomes. We believe our technique should apply to other k-mer or n-gram-based algorithms. ; This work was supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreements No. 639595); the Ministry of Economy of Spain under contract TIN2015-65316-P and Generalitat de Catalunya under contract 2014SGR1051; the ICREA Academia program; the BSC-CNS Severo Ochoa program (SEV-2015-0493); MIT via the NSF grant (CCF1725303); and travel support was provided by the MIT-Spain la Caixa Foundation Seed Fund. ; Peer Reviewed ; Postprint (published version)

© 2020 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ ; Conditional Restricted Boltzmann Machine (CRBM) is a promising candidate for a multidimensional system modeling that can learn a probability distribution over a set of data. It is a specific type of an artificial neural network with one input (visible) and one output (hidden) layer. Recently published works demonstrate that CRBM is a suitable mechanism for modeling multidimensional time series such as human motion, workload characterization, city traffic analysis. The process of learning and inference of these systems relies on linear algebra functions like matrix–matrix multiplication, and for higher data sets, they are very compute-intensive. In this paper, we present a configurable framework for CRBM based workloads for arbitrary large models. We show how to accelerate the learning process of CRBM with FPGAs and OpenCL, and we conduct an extensive scalability study for different model sizes and system configurations. We show significant improvement in performance/Watt for large models and batch sizes (from 1.51x up to 5.71x depending on the host configuration) when we use FPGA and OpenCL for the acceleration, and limited benefits for small models comparing to the state-of-the-art CPU solution. ; This work was supported by the European Research Council(ERC) under the European Union's Horizon 2020 research andinnovation programme (grant agreements No 639595); the Min-istry of Economy of Spain under contract TIN2015-65316-P andGeneralitat de Catalunya, Spain under contract 2014SGR1051;the ICREA, Spain Academia program; the BSC-CNS Severo Ochoaprogram, Spain (SEV-2015-0493) and Intel Corporation, UnitedStates ; Peer Reviewed ; Postprint (published version)