Max-plus matrix multiplication library for GPUs - MPMML

2019 Spring.Includes bibliographical references.Max-Plus algebra finds its applications in discrete event simulations, dynamic programming, biological sequence comparisons etc. Although there exist highly tuned libraries like CUDA Linear Algebra Subprograms (CuBLAS) [1] for matrix operations, they implement the standard matrix-multiplication (multiply-add) for floating points. We found no standard library for Max- Plus-Matrix-Multiplication (MPMM) on integers. Hence,we developed a highly tuned parallelized MPMM library kernel. We chose GPUs as hardware platform for this work because of their significantly more parallelism and arithmetic functional units as compared to CPUs. We designed this kernel to be portable across three successive Nvidia GPU architectures and it achieves performance in the range 3065 GOPs/S - 3631 GOPs/S on all of these architectures. We closely followed the benchmarking approach described by Volkov et al. [2] when they contributed to cuBLAS. This MPMM kernel can be part of a max-plus algebra library for GPUs and can help speed up Biological Sequence comparison applications like BPMax

Amplifying the Chirp: Using Deep Learning (U-Nets) to filter signal from noise in LIGO data

The direct detection of gravitational waves by LIGO has heralded a new era for astronomy and physics. Typically the gravitational waves observed by LIGO are dominated by noise. In this work we use Deep Convolutional Neural Networks (specifically U-Nets) to filter a clean signal from noisy data. We present two realizations of U-Net filters, the Noise2Clean U-Net filter which is trained using noisy and clean realizations of the same signal, as well as Noise2Noise U-Net which is trained on two separate noisy realization of the same signal. We find that the U-Nets successfully filter signal from noise. We also benchmark the performance of U-Nets by using them to detect the binary presence or absence of gravitational wave signals in data.Comment: 20 pages, 9 figures, comments welcom

Superradiance and the Spins of Black Holes from LIGO and X-ray binaries

Measurements of the spin of stellar mass black holes (BHs) are now possible both through LIGO observations of binary BH mergers and for BHs in X-ray binary systems. The spins of BHs as inferred from LIGO observations suggest that BH spins are on the lower end of what is expected for a ``flat'' distribution of spins, while those from BHs in X-ray binaries tend to be large. Superradiance, a process that can effectively reduce the spin of BHs before they merge, could explain the lower observed spins in binary BH mergers for a non self-interacting light boson. In this paper, we use Bayesian analysis to infer the posterior probability distribution for the mass of a light boson that could fit LIGO data. We also analyze spins of BHs from X-ray binaries, and find that the X-ray binary data can be explained by superradiance due to a light boson with large self-interactions. We infer the mass range for such a boson that is consistent with the X-ray binary data.Comment: 10 pages, 5 Figures, comments welcom

Engineering bacteria to disperse bacterial biofilms

Biofilms are aggregates of bacteria embedded in a self-produced matrix. In nature most bacteria exist in the form of biofilms. They are exceptionally resistant to environmental stress. They can survive harsh conditions such as starvation and desiccation, and can withstand conventional antimicrobial agents. Bacteria in the form of biofilms cause many diseases in humans, animals and plants. Formation of biofilms leads to corrosion of pipelines incurring huge losses in industries. The major focus of the research in microbiology is now shifting to biofilms. Researchers are exploring various strategies to combat biofilms. Among different methods of combating biofilms, biological methods offer some advantages over other methods. The biological methods make use of the existing signaling pathways in bacteria and bring about efficient disruption of the target biofilm without having to add any artificially synthesized compound. These methods are particularly attractive in treating biofilm-induced diseases. In this project I have proposed three different strategies of engineering Escherichia coli to disrupt the biofilms of E. coli and Staphylococcus epidermidis. The anti-biofilm agent used in all the strategies is Dispersin B, an enzyme that hydrolyzes poly-N-acetyl glucosamine found in the matrix of the target biofilms. Hydrolysis of this polymer leads to disruption of the biofilm. The first strategy makes use of the N-acetyl glucosamine signaling and chemotaxis pathway of E. coli to detect the target biofilm and to synthesize Dispersin B. The product of the action of Dispersin B is N-acetyl glucosamine, which acts as an inducer to elevate the synthesis of Dispersin B. The second strategy exploits the biofilm-specific pattern of gene expression in E. coli biofilms. The engineered bacterium expresses Dispersin B from the promoter of a gene that is multi-fold activated when the bacterium acquires the biofilm lifestyle. It incorporates itself in the target biofilm and disrupts the biofilm through the action of Dispersin B. The third strategy is specifically aimed at disrupting S. epidermidis biofilms. It uses the agr quorum-sensing system of S. epidermidis to detect the target biofilm and to synthesize Dispersin B. The strategies proposed in this project have been partially successful. I have demonstrated that E. coli can be engineered to express and secrete Dispersin B, which can disrupt the target biofilm efficiently. The strategies proposed in this project are versatile in application. They can be modified to engineer similar biological systems against biofilms of other species of bacteria. In the course of accomplishing the objective of this project I have explored some unknown aspects of the N-acetyl glucosamine signaling in E. coli. The analysis of the transcriptome of E. coli biofilms has revealed the up regulation of redox stress-associated genes in the attached cells of E. coli biofilms. These interesting findings can lead us to explore further into bacterial signaling and biofilm formation