HSPT

Wang, Zhe; Li, Jianjun; Wu, Chengyou; Yang, Dongyan; Wang, Zhenjiang; Hsu, Wei-Chung; Li, Bin; Guan, Yong Liang

doi:10.1145/2731186.2731188

Cited by 7 publications

(1 citation statement)

References 18 publications

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“…As a result, it appears that the calculations happen “out of the memory”, a technique called “ out-of-core ” in computer science. Mmap has been extensively used in many computing fields such as high-performance computing (Van Essen et al 2013; Song et al 2016), graph computation (Lin et al 2014), system virtual machine (Wang et al 2015), and databases (Lou and Ludewigt 2015). Mmap has also been used in bioinformatics in recent years, including the indexing of next-generating sequencing reads (Salavert et al 2016) and tree-based backward search (Salavert et al 2015).…”

mentioning

confidence: 99%

OCMA: Fast, Memory-Efficient Factorization of Prohibitively Large Relationship Matrices

Xiong

Zhang

Platt

et al. 2019

G3 Genes|Genomes|Genetics

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Matrices representing genetic relatedness among individuals (i.e., Genomic Relationship Matrices, GRMs) play a central role in genetic analysis. The eigen-decomposition of GRMs (or its alternative that generates fewer top singular values using genotype matrices) is a necessary step for many analyses including estimation of SNP-heritability, Principal Component Analysis (PCA), and genomic prediction. However, the GRMs and genotype matrices provided by modern biobanks are too large to be stored in active memory. To accommodate the current and future “bigger-data”, we develop a disk-based tool, Out-of-Core Matrices Analyzer (OCMA), using state-of-the-art computational techniques that can nimbly perform eigen and Singular Value Decomposition (SVD) analyses. By integrating memory mapping (mmap) and the latest matrix factorization libraries, our tool is fast and memory-efficient. To demonstrate the impressive performance of OCMA, we test it on a personal computer. For full eigen-decomposition, it solves an ordinary GRM (N = 10,000) in 55 sec. For SVD, a commonly used faster alternative of full eigen-decomposition in genomic analyses, OCMA solves the top 200 singular values (SVs) in half an hour, top 2,000 SVs in 0.95 hr, and all 5,000 SVs in 1.77 hr based on a very large genotype matrix (N = 1,000,000, M = 5,000) on the same personal computer. OCMA also supports multi-threading when running in a desktop or HPC cluster. Our OCMA tool can thus alleviate the computing bottleneck of classical analyses on large genomic matrices, and make it possible to scale up current and emerging analytical methods to big genomics data using lightweight computing resources.

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mentioning

confidence: 99%

OCMA: Fast, Memory-Efficient Factorization of Prohibitively Large Relationship Matrices

Xiong

Zhang

Platt

et al. 2019

G3 Genes|Genomes|Genetics

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On-Demand Triggered Memory Management Unit in Dynamic Binary Translator

Xie,

Li,

Yan

et al. 2023

Lecture Notes in Computer Science

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SofTEE: Software-Based Trusted Execution Environment for User Applications

Lee

Park

2020

IEEE Access

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Commodity operating systems are considered vulnerable. Therefore, when an application handles security-sensitive data, it is highly recommended to run the application in a trusted execution environment. In response to this demand, hardware-based trusted execution environments such as Intel SGX and ARM TrustZone have been developed in commodity computers. However, hardware-based approaches cannot be quickly upgraded to address design vulnerabilities or to reflect customer feedback. In this paper, we propose SofTEE, a software framework to support a trusted execution environment for user applications. For a trusted execution environment, SofTEE should support memory isolation and attestation. For memory isolation, SofTEE relies on kernel deprivileging which delegates the execution of privileged operations such as memory management, from a kernel to a special module called a security monitor. To reduce the overhead of switching between the deprivileged kernel and the security monitor, SofTEE proposes an efficient management mechanism of the address space identifier. SofTEE supports attestation by assuming minimal hardware functionalities of random entropy and root of trust. The main challenge of SofTEE is to guarantee security properties like confidentiality and integrity of security-sensitive applications. For security analysis, we have identified security invariants that SofTEE should meet for confidentiality and integrity guarantees. Based on the security invariants, we have designed and prototyped each component of SofTEE on a Raspberry Pi 3 board. SofTEE produces about 3% overhead in case of a security-sensitive application with long execution time and 23% overhead in case of a security-sensitive application with short execution time.

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HSPT

Cited by 7 publications

References 18 publications

OCMA: Fast, Memory-Efficient Factorization of Prohibitively Large Relationship Matrices

OCMA: Fast, Memory-Efficient Factorization of Prohibitively Large Relationship Matrices

On-Demand Triggered Memory Management Unit in Dynamic Binary Translator

SofTEE: Software-Based Trusted Execution Environment for User Applications

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