A Survey of Processing Systems for Phylogenetics and Population Genetics

The COVID-19 pandemic brought Bioinformatics into the spotlight, revealing that several existing methods, algorithms, and tools were not well prepared to handle large amounts of genomic data efficiently. This led to prohibitively long execution times and the need to reduce the extent of analyses to obtain results in a reasonable amount of time. In this survey, we review available high-performance computing and hardware-accelerated systems based on FPGA and GPU technology. Optimized and hardware-accelerated systems can conduct more thorough analyses considerably faster than pure software implementations, allowing to reach important conclusions in a timely manner to drive scientific discoveries. We discuss the reasons that are currently hindering high-performance solutions from being widely deployed in real-world biological analyses and describe a research direction that can pave the way to enable this

Increasing Flexibility of FPGA-based CNN Accelerators with Dynamic Partial Reconfiguration

Convolutional Neural Networks (CNN) are widely used for image classification and have achieved significantly accurate performance in the last decade. However, they require computationally intensive operations for embedded applications. In recent years, FPGA-based CNN accelerators have been proposed to improve energy efficiency and throughput. While dynamic partial reconfiguration (DPR) is increasingly used in CNN accelerators, the performance of dynamically reconfigurable accelerators is usually lower than the performance of pure static FPGA designs. This work presents a dynamically reconfigurable CNN accelerator architecture that does not sacrifice throughput performance or classification accuracy. The proposed accelerator is composed of reconfigurable macroblocks and dynamically utilizes the device resources according to model parameters. Moreover, we devise a novel approach, to the best of our knowledge, to hide the computations of the pooling layers inside the convolutional layers, thereby further improving throughput. Using the proposed architecture and DPR, different CNN architectures can be realized on the same FPGA with optimized throughput and accuracy. The proposed architecture is evaluated by implementing two different LeNet CNN models trained by different datasets and classifying different classes. Experimental results show that the implemented design achieves higher throughput than current LeNet FPGA accelerators

SweepNet:A Lightweight CNN Architecture for the Classification of Adaptive Genomic Regions

The accurate identification of positive selection in genomes represents a challenge in the field of population genomics. Several recent approaches have cast this problem as an image classification task and employed Convolutional Neural Networks (CNNs). However, limited efforts have been placed on discovering a practical CNN architecture that can classify images visualizing raw genomic data in the presence of population bottlenecks, migration, and recombination hotspots, factors that typically confound the identification and localization of adaptive genomic regions. In this work, we present SweepNet, a new CNN architecture that resulted from a thorough hyper-parameter-based architecture exploration process. SweepNet has a higher training efficiency than existing CNNs and requires considerably less epochs to achieve high validation accuracy. Furthermore, it performs consistently better in the presence of confounding factors, generating models with higher validation accuracy and lower top-1 error rate for distinguishing between neutrality and a selective sweep. Unlike existing network architectures, the number of trainable parameters of SweepNet remains constant irrespective of the sample size and number of Single Nucleotide Polymorphisms, which reduces the risk of overfitting and leads to more efficient training for large datasets. Our SweepNet implementation is available for download at: https://github.com/Zhaohq96/SweepNet

Coupling SIMD and SIMT Architectures to Boost Performance of a Phylogeny-aware Alignment Kernel

Background: Aligning short DNA reads to a reference sequence alignment is a prerequisite for detecting their biological origin and analyzing them in a phylogenetic context. With the PaPaRa tool we introduced a dedicated dynamic programming algorithm for simultaneously aligning short reads to reference alignments and corresponding evolutionary reference trees. The algorithm aligns short reads to phylogenetic profiles that correspond to the branches of such a reference tree. The algorithm needs to perform an immense number of pairwise alignments. Therefore, we explore vector intrinsics and GPUs to accelerate the PaPaRa alignment kernel. Results: We optimized and parallelized PaPaRa on CPUs and GPUs. Via SSE 4.1 SIMD (Single Instruction, Multiple Data) intrinsics for x86 SIMD architectures and multi-threading, we obtained a 9-fold acceleration on a single core as well as linear speedups with respect to the number of cores. The peak CPU performance amounts to 18.1 GCUPS (Giga Cell Updates per Second) using all four physical cores on an Intel i7 2600 CPU running at 3.4 GHz. The average CPU performance (averaged over all test runs) is 12.33 GCUPS. We also used OpenCL to execute PaPaRa on a GPU SIMT (Single Instruction, Multiple Threads) architecture. A NVIDIA GeForce 560 GPU delivered peak and average performance of 22.1 and 18.4 GCUPS respectively. Finally, we combined the SIMD and SIMT implementations into a hybrid CPU-GPU system that achieved an accumulated peak performance of 33.8 GCUPS. Conclusions: This accelerated version of PaPaRa (available at www.exelixis-lab.org/software.html) provides a significant performance improvement that allows for analyzing larger datasets in less time. We observe that state-of-the-art SIMD and SIMT architectures deliver comparable performance for this dynamic programming kernel when the “competing programmer approach” is deployed. Finally, we show that overall performance can be substantially increased by designing a hybrid CPU-GPU system with appropriate load distribution mechanisms

EDRA:A Hardware-assisted Decoupled Access/Execute Framework on the Digital Market

EDRA was an Horizon 2020 FET Launchpad project that focused on the commercialization of the Decoupled Access Execution Reconfigurable (DAER) framework - developed within the FET-HPC EXTRA project - on Amazon's Elastic Cloud (EC2) Compute FPGA-based infrastructure. The delivered framework encapsulates DAER into a EC2 virtual machine (VM), and uses a simple, directive-based, high-level application programming interface (API) to facilitate application mapping to the underlying hardware architecture. EDRA's Minimum Viable Product (MVP) is an accelerator for the Phylogenetic Likelihood Function (PLF), one of the cornerstone functions in most phylogenetic inference tools, achieving up to 8x performance improvement compared to optimized software implementations. Towards entering the market, research revealed that Europe is an extremely promising geographic region for focusing the project efforts on dissemination, MVP promotion and advertisement

Genome-wide scans for selective sweeps using convolutional neural networks

Motivation: Recent methods for selective sweep detection cast the problem as a classification task and use summary statistics as features to capture region characteristics that are indicative of a selective sweep, thereby being sensitive to confounding factors. Furthermore, they are not designed to perform whole-genome scans or to estimate the extent of the genomic region that was affected by positive selection; both are required for identifying candidate genes and the time and strength of selection.Results: We present ASDEC (https://github.com/pephco/ASDEC), a neural-network-based framework that can scan whole genomes for selective sweeps. ASDEC achieves similar classification performance to other convolutional neural network-based classifiers that rely on summary statistics, but it is trained 10× faster and classifies genomic regions 5× faster by inferring region characteristics from the raw sequence data directly. Deploying ASDEC for genomic scans achieved up to 15.2× higher sensitivity, 19.4× higher success rates, and 4× higher detection accuracy than state-of-the-art methods. We used ASDEC to scan human chromosome 1 of the Yoruba population (1000Genomes project), identifying nine known candidate genes