@inproceedings {497, title = {HALO 1.0: A Hardware-agnostic Accelerator Orchestration Framework for Enabling Hardware-agnostic Programming with True Performance Portability for Heterogeneous HPC}, year = {Under Review}, abstract = {

Hardware-agnostic programming with high performance portability will be the bedrock for realizing the ubiquitous adoption of emerging accelerator technologies in future heterogeneous high-performance computing (HPC) systems, which is the key to achieving the next level of HPC performance on an expanding accelerator landscape. In this paper, we present HALO 1.0, an open-ended extensible multi-agent software framework, that implements a set of proposed hardware-agnostic accelerator orchestration (HALO) principles and a novel compute-centric message passing interface (C2MPI) specification for enabling the portable and performance-optimized execution of hardware-agnostic application codes across heterogeneous accelerator resources. The experiment results of evaluating eight widely used HPC subroutines based on Intel Xeon E5-2620 v4 CPUs, Intel Arria 10 GX FPGAs, and NVIDIA GeForce RTX 2080 Ti GPUs show that HALO 1.0 allows the same hardware-agnostic application codes of the HPC kernels, without any change, to run across all the computing devices with a consistently maximum performance portability score of 1.0, which is 2x-861,883x higher than the OpenCL-based solution that suffers from an unstably low performance portability score.

}, keywords = {psclab}, url = {https://arxiv.org/pdf/2011.10896.pdf}, author = {Michael Riera and Bank-Tavakoli, Erfan and Masudul Hassan Quraishi and Fengbo Ren} } @inproceedings {Bordeaux, France, title = {A Data-Driven Approach for Automated Integrated Circuit Segmentation of Scan Electron Microscopy Images}, year = {2022}, month = {10/2022}, address = {Bordeaux, France}, abstract = {

This paper proposes an automated data-driven integrated
circuit segmentation approach of scan electron microscopy
(SEM) images inspired by state-of-the-art CNN-based image
perception methods. Based on the requirements derived from
real industry applications, we take wire segmentation and via
detection algorithms to generate integrated circuit segmentation
maps from SEMs in our approach. On SEM images
collected in the industrial applications, our method achieves
an average of 50.71 on Electrically Significant Difference
(ESD) in the wire segmentation task and 99.05\% F1 score
in the via detection task, which achieves about 85\% and 8\%
improvements over the reference method, respectively.

}, keywords = {psclab}, author = {Zifan Yu and Bruno Machado Trindade and Michael Green and Zhikang Zhang and Pullela Sneha and Bank-Tavakoli, Erfan and Christopher Pawlowicz and Ren, Fengbo} } @inproceedings {Virtual Event, title = {FSCHOL: An OpenCL-based HPC Framework for Accelerating Sparse Cholesky Factorization on FPGAs}, year = {2021}, month = {10/2021}, pages = {209-220}, address = {Virtual Event}, abstract = {

The proposed FSCHOL framework consists of an FPGA kernel implementing a throughput-optimized hardware architecture for accelerating the supernodal multifrontal algorithm for sparse Cholesky factorization and a host program implementing a novel scheduling algorithm for finding the optimal execution order of supernodes computations for an elimination tree on the FPGA to eliminate the need for off-chip memory access for storing intermediate results. Moreover, the proposed scheduling algorithm minimizes on-chip memory requirements for buffering intermediate results by resolving the dependency of parent nodes in an elimination tree through temporal parallelism. Experiment results for factorizing a set of sparse matrices in various sizes from SuiteSparse Matrix Collection show that the proposed FSCHOL implemented on an Intel Stratix 10 GX FPGA development board achieves on average 5.5\× and 9.7\× higher performance and 10.3\× and 24.7\× lower energy consumption than implementations of CHOLMOD on an Intel Xeon E5-2637 CPU and an NVIDIA V100 GPU, respectively.

}, keywords = {psclab}, doi = {10.1109/SBAC-PAD53543.2021.00032}, author = {Erfan Bank-Tavakoli and Michael Riera and Masudul Hassan Quraishi and Fengbo Ren} } @article {498, title = {A Survey of System Architectures and Techniques for FPGA Virtualization}, journal = { IEEE Transactions on Parallel and Distributed Systems}, volume = {32}, year = {2021}, month = {09/2021}, pages = {2216-2230}, abstract = {

FPGA accelerators are gaining increasing attention in both cloud and edge computing because of their hardware flexibility, high computational throughput, and low power consumption. However, the design flow of FPGAs often requires specific knowledge of the underlying hardware, which hinders the wide adoption of FPGAs by application developers. Therefore, the virtualization of FPGAs becomes extremely important to create a useful abstraction of the hardware suitable for application developers. Such abstraction also enables the sharing of FPGA resources among multiple users and accelerator applications, which is important because, traditionally, FPGAs have been mostly used in single-user, single-embedded-application scenarios. There are many works in the field of FPGA virtualization covering different aspects and targeting different application areas. In this survey, we review the system architectures used in the literature for FPGA virtualization. In addition, we identify the primary objectives of FPGA virtualization, based on which we summarize the techniques for realizing FPGA virtualization. This survey helps researchers to efficiently learn about FPGA virtualization research by providing a comprehensive review of the existing literature.

}, keywords = {psclab}, issn = {1045-9219}, doi = {10.1109/TPDS.2021.3063670}, author = {Masudul Hassan Quraishi and Erfan Bank-Tavakoli and Fengbo Ren} } @inproceedings {360, title = {Are FPGAs Suitable for Edge Computing?}, year = {2018}, month = {07/2018}, address = {BOSTON, MA}, abstract = {

The rapid growth of Internet-of-things (IoT) and artificial intelligence applications have called forth a new computing paradigm\–edge computing. In this paper, we study the suitability of deploying FPGAs for edge computing from the perspectives of throughput sensitivity to workload size, architectural adaptiveness to algorithm characteristics, and energy efficiency. This goal is accomplished by conducting comparison experiments on an Intel Arria 10 GX1150 FPGA and an Nvidia Tesla K40m GPU. The experiment results imply that the key advantages of adopting FPGAs for edge computing over GPUs are three-fold: 1) FPGAs can provide a consistent throughput invariant to the size of application workload, which is critical to aggregating individual service requests from various IoT sensors; (2) FPGAs offer both spatial and temporal parallelism at a fine granularity and a massive scale, which guarantees a consistently high performance for accelerating both high-concurrency and high-dependency algorithms; and (3) FPGAs feature 3\– 4 times lower power consumption and up to 30.7 times better energy efficiency, offering better thermal stability and lower energy cost per functionality.

}, keywords = {psclab}, author = {Saman Biookaghazadeh and Ming Zhao and Fengbo Ren} } @inproceedings {297, title = {Kaleido: Enabling Efficient Scientific Data Processing on Big-Data Systems}, year = {2017}, pages = {1{\textendash}10}, publisher = {IEEE}, abstract = {

Abstract:

Big-Data systems are increasingly important for solving the data-driven problems in many science domains including geosciences. However, existing big- data systems cannot support the efficient processing of self-describing data formats such as NetCDF which are commonly used by scientific communities for data distribution and sharing. This limitation presents a serious hurdle to the further adoption of big-data systems by science domains. This paper presents Kaleido, a solution to this problem by enabling big- data systems to efficiently store and process scientific data. Specifically, it enables Hadoop to directly store NetCDF data on HDFS, and process them in MapReduce using convenient APIs. It also enables Hive to support queries on NetCDF data, transparent to the users. Moreover, it employs optimizations tailored to scientific data, particularly dimension-aware layout which allows efficient execution of subset queries targeting any dimension of the multi- dimensional data. The paper presents a comprehensive evaluation of Kaleido using representative queries on a typical geoscientific dataset. The results show that Kaleido achieves substantial speedup and space saving compared to existing solutions for storing and processing NetCDF data on Hadoop, and it also substantially outperforms the state-of-the-art solutions for supporting subset queries on scientific data.
}, author = {Biookaghazadeh, Saman}, editor = {Zhou, Shujia and Zhao, Ming} } @booklet {279, title = {Techniques and systems for local independent failure domains}, year = {2017}, abstract = {

Techniques and systems are described for enabling local independent failure domains in a host server or datacenter environment. Included is a locally-attached independent failure device (LA-IFD) with an independent data buffer and a local communications bus for attaching to a host server. Techniques for a communications protocol between the LA-IFD and its host server are provided, including: receiving a request to write a data segment to persistent storage; synchronously storing the data segment in a buffered data segment at the LA-IFD and initiating an asynchronous update of the data segment at a remote storage system; sending a write acknowledgement indicating completion to the requestor; and, after receiving a completion notification from the remote storage system, removing the buffered data segment from the LA-IFD. In some cases, techniques allow a host server and LA-IFD pair to monitor one another for failures and implement a modified protocol in the event of unavailability.

}, keywords = {other}, author = {Rangaswami, Raju and Biookaghazadeh, Saman and Lyons, Steven} } @inproceedings {hubara2016binarized, title = {Binarized neural networks}, year = {2016}, pages = {4107{\textendash}4115}, keywords = {Deep Learning, ref}, author = {Hubara, Itay and Courbariaux, Matthieu and Soudry, Daniel and El-Yaniv, Ran and Bengio, Yoshua} } @article {courbariaux2016binarized, title = {Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1}, journal = {arXiv preprint arXiv:1602.02830}, year = {2016}, keywords = {Deep Learning, ref}, author = {Courbariaux, Matthieu and Hubara, Itay and Soudry, Daniel and El-Yaniv, Ran and Bengio, Yoshua} } @inproceedings {courbariaux2015binaryconnect, title = {Binaryconnect: Training deep neural networks with binary weights during propagations}, year = {2015}, pages = {3123{\textendash}3131}, keywords = {Deep Learning, ref}, author = {Courbariaux, Matthieu and Bengio, Yoshua and David, Jean-Pierre} } @article {lecun2015deep, title = {Deep learning}, journal = {Nature}, volume = {521}, number = {7553}, year = {2015}, pages = {436{\textendash}444}, publisher = {Nature Research}, keywords = {Deep Learning, ref}, author = {LeCun, Yann and Bengio, Yoshua and Hinton, Geoffrey} } @inproceedings {mousavi2015deep, title = {A deep learning approach to structured signal recovery}, year = {2015}, pages = {1336{\textendash}1343}, publisher = {IEEE}, keywords = {Compressive Sensing, ref}, author = {Mousavi, Ali and Patel, Ankit B and Baraniuk, Richard G} } @inproceedings {278, title = {Enabling scientific data storage and processing on big-data systems}, year = {2015}, pages = {1978{\textendash}1984}, publisher = {IEEE}, abstract = {

Big-data systems are increasingly important for solving the data-driven problems in many science domains including geosciences. However, existing big-data systems cannot support the self-describing data formats such as NetCDF which are commonly used by scientific communities for data distribution and sharing. This limitation presents a serious hurdle to the further adoption of big-data systems by science domains and prevents scientific users from leveraging these systems to improve their productivity. This paper presents a solution to this problem by enabling big-data systems to directly store and process scientific data. Specifically, it enables Hadoop to efficiently store NetCDF data on HDFS and process them in MapReduce using convenient APIs. It also enables Hive to support standard queries on NetCDF data, transparently to users. The paper also presents an evaluation of the proposed solution using several representative queries on a typical geoscientific dataset. The results show that the proposed approach achieves substantial speedup (up to 20 times) and space saving (83\% reduction), compared to the traditional approach which has to convert NetCDF data to CSV format for Hadoop and Hive to use them.

}, keywords = {ref}, author = {Biookaghazadeh, Saman and Xu, Yiqi and Zhou, Shujia and Zhao, Ming} } @article {hegde2015numax, title = {NuMax: A convex approach for learning near-isometric linear embeddings}, journal = {IEEE Transactions on Signal Processing}, volume = {63}, number = {22}, year = {2015}, pages = {6109{\textendash}6121}, publisher = {IEEE}, keywords = {Compressive Sensing, ref}, author = {Hegde, Chinmay and Sankaranarayanan, Aswin C and Yin, Wotao and Baraniuk, Richard G} } @article {qaisar2013compressive, title = {Compressive sensing: From theory to applications, a survey}, journal = {Journal of Communications and networks}, volume = {15}, number = {5}, year = {2013}, pages = {443{\textendash}456}, publisher = {IEEE}, keywords = {Compressive Sensing, ref}, author = {Qaisar, Saad and Bilal, Rana Muhammad and Iqbal, Wafa and Naureen, Muqaddas and Lee, Sungyoung} } @article {277, title = {MemBuilder: a web-based graphical interface to build heterogeneously mixed membrane bilayers for the GROMACS biomolecular simulation program}, journal = {Bioinformatics}, volume = {30}, year = {2013}, pages = {439{\textendash}441}, abstract = {

Motivation:\ Molecular dynamics (MD) simulations have had a profound impact on studies of membrane proteins during past two decades, but the accuracy of MD simulations of membranes is limited by the quality of membrane models and the applied force fields. Membrane models used in MD simulations mostly contain one kind of lipid molecule. This is far from reality, for biological membranes always contain more than one kind of lipid molecule. Moreover, the lipid composition and their distribution are functionally important. As a result, there is a necessity to prepare more realistic lipid membranes containing different types of lipids at physiological concentrations.

Results:\ To automate and simplify the building process of heterogeneous lipid bilayers as well as providing molecular topologies for included lipids based on both united and all-atom force fields, we provided MemBuilder as a web-based graphical user interface.

Availability and implementation:\ MemBuilder is a free web server available\ 

}, keywords = {other}, author = {Ghahremanpour, Mohammad Mehdi and Arab, Seyed Shahriar and Biookaghazadeh, Saman and Zhang, Jin and van der Spoel, David} } @inbook {bengio2012practical, title = {Practical recommendations for gradient-based training of deep architectures}, booktitle = {Neural networks: Tricks of the trade}, year = {2012}, pages = {437{\textendash}478}, publisher = {Springer}, organization = {Springer}, keywords = {Deep Learning, ref}, author = {Bengio, Yoshua} }