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80 results on '"Kim, Jeremie S."'

53. Security Analysis of the Silver Bullet Technique for RowHammer Prevention

Author

Ya��l��k����, Abdullah Giray, Kim, Jeremie S., Devaux, Fabrice, and Mutlu, Onur

Subjects
FOS: Computer and information sciences, Computer Science - Cryptography and Security, GeneralLiterature_INTRODUCTORYANDSURVEY, Hardware Architecture (cs.AR), Computer Science - Hardware Architecture, Cryptography and Security (cs.CR)
Abstract

The purpose of this document is to study the security properties of the Silver Bullet algorithm against worst-case RowHammer attacks. We mathematically demonstrate that Silver Bullet, when properly configured and implemented in a DRAM chip, can securely prevent RowHammer attacks. The demonstration focuses on the most representative implementation of Silver Bullet, the patent claiming many implementation possibilities not covered in this demonstration. Our study concludes that Silver Bullet is a promising RowHammer prevention mechanism that can be configured to operate securely against RowHammer attacks at various efficiency-area tradeoff points, supporting relatively small hammer count values (e.g., 1000) and Silver Bullet table sizes (e.g., 1.06KB)., 40 pages

Published
2021

54. AirLift: A Fast and Comprehensive Technique for Remapping Alignments between Reference Genomes

Author

Kim, Jeremie S., Firtina, Can, Cavlak, Meryem Banu, Senol Cali, Damla, Hajinazar, Nastaran, Alser, Mohammed, Alkan, Can, and Mutlu, Onur

Subjects
Genome Read Mapping, Remapping, LiftOver, Genome Assembly, Crossmap
Abstract

As genome sequencing tools and techniques improve, researchers are able to incrementally assemble more accurate reference genomes, which enable sensitivity in read mapping and downstream analysis such as variant calling. A more sensitive downstream analysis is critical for a better understanding of the genome donor (e.g., health characteristics). Therefore, read sets from sequenced samples should ideally be mapped to the latest available reference genome that represents the most relevant population. Unfortunately, the increasingly large amount of available genomic data makes it prohibitively expensive to fully re-map each read set to its respective reference genome every time the reference is updated. There are several tools that attempt to accelerate the process of updating a read data set from one reference to another (i.e., remapping). However, if a read maps to a region in the old reference that does not appear with a reasonable degree of similarity in the new reference, the read cannot be remapped. We find that, as a result of this drawback, a significant portion of annotations are lost when using state-of-the-art remapping tools. To address this major limitation in existing tools, we propose AirLift, a fast and comprehensive technique for remapping alignments from one genome to another. Compared to the state-of-the-art method for remapping reads (i.e., full mapping), AirLift reduces 1) the number of reads that need to be fully mapped to the new reference by up to 99.99\% and 2) the overall execution time to remap read sets between two reference genome versions by 6.7x, 6.6x, and 2.8x for large (human), medium (C. elegans), and small (yeast) reference genomes, respectively. We validate our remapping results with GATK and find that AirLift provides similar accuracy in identifying ground truth SNP and INDEL variants as the baseline of fully mapping a read set., arXiv

Published
2021

57. CODIC: A Low-Cost Substrate for Enabling Custom In-DRAM Functionalities and Optimizations

Author

Orosa, Lois, primary, Wang, Yaohua, additional, Sadrosadati, Mohammad, additional, Kim, Jeremie S., additional, Patel, Minesh, additional, Puddu, Ivan, additional, Luo, Haocong, additional, Razavi, Kaveh, additional, Gomez-Luna, Juan, additional, Hassan, Hasan, additional, Mansouri-Ghiasi, Nika, additional, Ghose, Saugata, additional, and Mutlu, Onur, additional

Published
2021
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63. GenASM: A High-Performance, Low-Power Approximate String Matching Acceleration Framework for Genome Sequence Analysis

Author

Cali, Damla Senol, primary, Kalsi, Gurpreet S., additional, Bingol, Zulal, additional, Firtina, Can, additional, Subramanian, Lavanya, additional, Kim, Jeremie S., additional, Ausavarungnirun, Rachata, additional, Alser, Mohammed, additional, Gomez-Luna, Juan, additional, Boroumand, Amirali, additional, Nori, Anant, additional, Scibisz, Allison, additional, Subramoney, Sreenivas, additional, Alkan, Can, additional, Ghose, Saugata, additional, and Mutlu, Onur, additional

Published
2020
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69. CROW

Author

Hassan, Hasan, primary, Patel, Minesh, additional, Kim, Jeremie S., additional, Yaglikci, A. Giray, additional, Vijaykumar, Nandita, additional, Ghiasi, Nika Mansouri, additional, Ghose, Saugata, additional, and Mutlu, Onur, additional

Published
2019
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71. GRIM-Filter: Fast seed location filtering in DNA read mapping using processing-in-memory technologies

Author

Ergin, Oğuz, Senol Cali, Damla, Kim, Jeremie S., Xin, Hongyi, Lee, Donghyuk, Ghose, Saugata, Alser, Mohammed, Hassan, Hasan, Alkan, Can, Mutlu, Onur, Ergin, Oğuz, Senol Cali, Damla, Kim, Jeremie S., Xin, Hongyi, Lee, Donghyuk, Ghose, Saugata, Alser, Mohammed, Hassan, Hasan, Alkan, Can, and Mutlu, Onur

Abstract

Background: Seed location filtering is critical in DNA read mapping, a process where billions of DNA fragments (reads) sampled from a donor are mapped onto a reference genome to identify genomic variants of the donor. State-of-the-art read mappers 1) quickly generate possible mapping locations for seeds (i.e., smaller segments) within each read, 2) extract reference sequences at each of the mapping locations, and 3) check similarity between each read and its associated reference sequences with a computationally-expensive algorithm (i.e., sequence alignment) to determine the origin of the read. A seed location filter comes into play before alignment, discarding seed locations that alignment would deem a poor match. The ideal seed location filter would discard all poor match locations prior to alignment such that there is no wasted computation on unnecessary alignments. Results: We propose a novel seed location filtering algorithm, GRIM-Filter, optimized to exploit 3D-stacked memory systems that integrate computation within a logic layer stacked under memory layers, to perform processing-in-memory (PIM). GRIM-Filter quickly filters seed locations by 1) introducing a new representation of coarse-grained segments of the reference genome, and 2) using massively-parallel in-memory operations to identify read presence within each coarse-grained segment. Our evaluations show that for a sequence alignment error tolerance of 0.05, GRIM-Filter 1) reduces the false negative rate of filtering by 5.59x-6.41x, and 2) provides an end-to-end read mapper speedup of 1.81x-3.65x, compared to a state-of-the-art read mapper employing the best previous seed location filtering algorithm. Conclusion: GRIM-Filter exploits 3D-stacked memory, which enables the efficient use of processing-in-memory, to overcome the memory bandwidth bottleneck in seed location filtering. We show that GRIM-Filter significantly improves the performance of a state-of-the-art read mapper. GRIM-Filter is a universal, SRC ; NIH [HG006004]

Published
2019

78. Nanopore sequencing technology and tools for genome assembly: computational analysis of the current state, bottlenecks and future directions.

Author

Cali, Damla Senol, Kim, Jeremie S, Ghose, Saugata, Alkan, Can, and Mutlu, Onur

Subjects
ERROR rates, SEQUENCE analysis, TECHNOLOGY
Abstract

Nanopore sequencing technology has the potential to render other sequencing technologies obsolete with its ability to generate long reads and provide portability. However, high error rates of the technology pose a challenge while generating accurate genome assemblies. The tools used for nanopore sequence analysis are of critical importance, as they should overcome the high error rates of the technology. Our goal in this work is to comprehensively analyze current publicly available tools for nanopore sequence analysis to understand their advantages, disadvantages and performance bottlenecks. It is important to understand where the current tools do not perform well to develop better tools. To this end, we (1) analyze the multiple steps and the associated tools in the genome assembly pipeline using nanopore sequence data, and (2) provide guidelines for determining the appropriate tools for each step. Based on our analyses, we make four key observations: (1) the choice of the tool for basecalling plays a critical role in overcoming the high error rates of nanopore sequencing technology. (2) Read-to-read overlap finding tools, GraphMap and Minimap, perform similarly in terms of accuracy. However, Minimap has a lower memory usage, and it is faster than GraphMap. (3) There is a trade-off between accuracy and performance when deciding on the appropriate tool for the assembly step. The fast but less accurate assembler Miniasm can be used for quick initial assembly, and further polishing can be applied on top of it to increase the accuracy, which leads to faster overall assembly. (4) The state-of-the-art polishing tool, Racon, generates high-quality consensus sequences while providing a significant speedup over another polishing tool, Nanopolish. We analyze various combinations of different tools and expose the trade-offs between accuracy, performance, memory usage and scalability. We conclude that our observations can guide researchers and practitioners in making conscious and effective choices for each step of the genome assembly pipeline using nanopore sequence data. Also, with the help of bottlenecks we have found, developers can improve the current tools or build new ones that are both accurate and fast, to overcome the high error rates of the nanopore sequencing technology. [ABSTRACT FROM AUTHOR]

Published
2019
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80. AirLift: A Fast and Comprehensive Technique for Remapping Alignments between Reference Genomes.

Author

Kim JS, Firtina C, Cavlak MB, Cali DS, Hajinazar N, Alser M, Alkan C, and Mutlu O

Abstract

AirLift is the first read remapping tool that enables users to quickly and comprehensively map a read set, that had been previously mapped to one reference genome, to another similar reference. Users can then quickly run a downstream analysis of read sets for each latest reference release. Compared to the state-of-the-art method for remapping reads (i.e., full mapping), AirLift reduces the overall execution time to remap read sets between two reference genome versions by up to 27.4×. We validate our remapping results with GATK and find that AirLift provides high accuracy in identifying ground truth SNP/INDEL variants.

Published
2024
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