Pub Date : 2019-09-01DOI: 10.1109/HPEC.2019.8916219
M. Harris, M. H. Langston, Pierre-David Létourneau, G. Papanicolaou, J. Ezick, R. Lethin
We present a fast, large-scale algorithm for the simulation of electromagnetic waves (Maxwell’s equations) in three-dimensional inhomogeneous media. The algorithm has a complexity of $O(Nlog (N))$ and runs in parallel. Numerical simulations show the rapid treatment of problems with tens of millions of unknowns on a small shared-memory cluster (≤ 16 cores).
{"title":"Fast Large-Scale Algorithm for Electromagnetic Wave Propagation in 3D Media","authors":"M. Harris, M. H. Langston, Pierre-David Létourneau, G. Papanicolaou, J. Ezick, R. Lethin","doi":"10.1109/HPEC.2019.8916219","DOIUrl":"https://doi.org/10.1109/HPEC.2019.8916219","url":null,"abstract":"We present a fast, large-scale algorithm for the simulation of electromagnetic waves (Maxwell’s equations) in three-dimensional inhomogeneous media. The algorithm has a complexity of $O(Nlog (N))$ and runs in parallel. Numerical simulations show the rapid treatment of problems with tens of millions of unknowns on a small shared-memory cluster (≤ 16 cores).","PeriodicalId":184253,"journal":{"name":"2019 IEEE High Performance Extreme Computing Conference (HPEC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124772697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/HPEC.2019.8916436
Mohamad Sindi, John R. Williams
We share experiences in implementing a containerbased HPC environment that could help sustain running HPC workloads on clusters. By running workloads inside containers, we are able to migrate them from cluster nodes anticipating hardware problems, to healthy nodes while the workloads are running. Migration is done using the CRIU tool with no application modification. No major interruption or overhead is introduced to the workload. Various real HPC applications are tested. Tests are done with different hardware node specs, network interconnects, and MPI implementations. We also benchmark the applications on containers and compare performance to native. Results demonstrate successful migration of HPC workloads inside containers with minimal interruption, while maintaining the integrity of the results produced. We provide several YouTube videos demonstrating the migration tests. Benchmarks also show that application performance on containers is close to native. We discuss some of the challenges faced during implementation and solutions adopted. To the best of our knowledge, we believe this work is the first to demonstrate successful migration of real MPI-based HPC workloads using CRIU and containers.
{"title":"Using Container Migration for HPC Workloads Resilience","authors":"Mohamad Sindi, John R. Williams","doi":"10.1109/HPEC.2019.8916436","DOIUrl":"https://doi.org/10.1109/HPEC.2019.8916436","url":null,"abstract":"We share experiences in implementing a containerbased HPC environment that could help sustain running HPC workloads on clusters. By running workloads inside containers, we are able to migrate them from cluster nodes anticipating hardware problems, to healthy nodes while the workloads are running. Migration is done using the CRIU tool with no application modification. No major interruption or overhead is introduced to the workload. Various real HPC applications are tested. Tests are done with different hardware node specs, network interconnects, and MPI implementations. We also benchmark the applications on containers and compare performance to native. Results demonstrate successful migration of HPC workloads inside containers with minimal interruption, while maintaining the integrity of the results produced. We provide several YouTube videos demonstrating the migration tests. Benchmarks also show that application performance on containers is close to native. We discuss some of the challenges faced during implementation and solutions adopted. To the best of our knowledge, we believe this work is the first to demonstrate successful migration of real MPI-based HPC workloads using CRIU and containers.","PeriodicalId":184253,"journal":{"name":"2019 IEEE High Performance Extreme Computing Conference (HPEC)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128319792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/HPEC.2019.8916446
M. H. Langston, M. Harris, Pierre-David Létourneau, R. Lethin, J. Ezick
The Combinatorial Multigrid (CMG) technique is a practical and adaptable solver and combinatorial preconditioner for solving certain classes of large, sparse systems of linear equations. CMG is similar to Algebraic Multigrid (AMG) but replaces large groupings of fine-level variables with a single coarse-level one, resulting in simple and fast interpolation schemes. These schemes further provide control over the refinement strategies at different levels of the solver hierarchy depending on the condition number of the system being solved [1]. While many pre-existing solvers may be able to solve large, sparse systems with relatively low complexity, inversion may require O(n2) space; whereas, if we know that a linear operator has $tilde{n}=O(n)$ nonzero elements, we desire to use O(n) space in order to reduce communication as much as possible. Being able to invert sparse linear systems of equations, asymptotically as fast as the values can be read from memory, has been identified by the Defense Advanced Research Projects Agency (DARPA) and the Department of Energy (DOE) as increasingly necessary for scalable solvers and energy-efficient algorithms [2], [3] in scientific computing. Further, as industry and government agencies move towards exascale, fast solvers and communication-avoidance will be more necessary [4], [5]. In this paper, we present an optimized implementation of the Combinatorial Multigrid in C using Petsc and analyze the solution of various systems using the CMG approach as a preconditioner on much larger problems than have been presented thus far. We compare the number of iterations, setup times and solution times against other popular preconditioners for such systems, including Incomplete Cholesky and a Multigrid approach in Petsc against common problems, further exhibiting superior performance by the CMG.1 2
{"title":"Combinatorial Multigrid: Advanced Preconditioners For Ill-Conditioned Linear Systems","authors":"M. H. Langston, M. Harris, Pierre-David Létourneau, R. Lethin, J. Ezick","doi":"10.1109/HPEC.2019.8916446","DOIUrl":"https://doi.org/10.1109/HPEC.2019.8916446","url":null,"abstract":"The Combinatorial Multigrid (CMG) technique is a practical and adaptable solver and combinatorial preconditioner for solving certain classes of large, sparse systems of linear equations. CMG is similar to Algebraic Multigrid (AMG) but replaces large groupings of fine-level variables with a single coarse-level one, resulting in simple and fast interpolation schemes. These schemes further provide control over the refinement strategies at different levels of the solver hierarchy depending on the condition number of the system being solved [1]. While many pre-existing solvers may be able to solve large, sparse systems with relatively low complexity, inversion may require O(n2) space; whereas, if we know that a linear operator has $tilde{n}=O(n)$ nonzero elements, we desire to use O(n) space in order to reduce communication as much as possible. Being able to invert sparse linear systems of equations, asymptotically as fast as the values can be read from memory, has been identified by the Defense Advanced Research Projects Agency (DARPA) and the Department of Energy (DOE) as increasingly necessary for scalable solvers and energy-efficient algorithms [2], [3] in scientific computing. Further, as industry and government agencies move towards exascale, fast solvers and communication-avoidance will be more necessary [4], [5]. In this paper, we present an optimized implementation of the Combinatorial Multigrid in C using Petsc and analyze the solution of various systems using the CMG approach as a preconditioner on much larger problems than have been presented thus far. We compare the number of iterations, setup times and solution times against other popular preconditioners for such systems, including Incomplete Cholesky and a Multigrid approach in Petsc against common problems, further exhibiting superior performance by the CMG.1 2","PeriodicalId":184253,"journal":{"name":"2019 IEEE High Performance Extreme Computing Conference (HPEC)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125475653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/HPEC.2019.8916504
Majid Rasouli, Vidhi Zala, R. Kirby, H. Sundar
Multigrid is one of the most effective methods for solving elliptic PDEs. It is algorithmically optimal and is robust when combined with Krylov methods. Algebraic multigrid is especially attractive due to its blackbox nature. This however comes at the cost of increased setup costs that can be significant in case of systems where the system matrix changes frequently making it difficult to amortize the setup cost. In this work, we investigate several strategies for performing lazy updates to the multigrid hierarchy corresponding to changes in the system matrix. These include delayed updates, value updates without changing structure, process local changes, and full updates. We demonstrate that in many cases, the overhead of building the AMG hierarchy can be mitigated for rapidly changing system matrices.
{"title":"Scalable Lazy-update Multigrid Preconditioners","authors":"Majid Rasouli, Vidhi Zala, R. Kirby, H. Sundar","doi":"10.1109/HPEC.2019.8916504","DOIUrl":"https://doi.org/10.1109/HPEC.2019.8916504","url":null,"abstract":"Multigrid is one of the most effective methods for solving elliptic PDEs. It is algorithmically optimal and is robust when combined with Krylov methods. Algebraic multigrid is especially attractive due to its blackbox nature. This however comes at the cost of increased setup costs that can be significant in case of systems where the system matrix changes frequently making it difficult to amortize the setup cost. In this work, we investigate several strategies for performing lazy updates to the multigrid hierarchy corresponding to changes in the system matrix. These include delayed updates, value updates without changing structure, process local changes, and full updates. We demonstrate that in many cases, the overhead of building the AMG hierarchy can be mitigated for rapidly changing system matrices.","PeriodicalId":184253,"journal":{"name":"2019 IEEE High Performance Extreme Computing Conference (HPEC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120894534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/HPEC.2019.8916495
M. Rogowski, Suha N. Kayum
Load balancing is a crucial factor affecting the performance of parallel applications. Improper work distribution leads to underutilization of computing resources and an unnecessary increase in runtime. This paper identifies the imbalance sources in reservoir simulation and characterizes them as static or dynamic. Simulation model properties that change over time, such as well management actions, are registered and correlated with performance characteristics hence identifying sources of imbalance. The results are exploratory and used to validate the current approach of static grid-to-process, and well-to-process assignment widely used in commercial parallel reservoir simulators. Areas in which implementing dynamic load balancing would be worthwhile are identified.
{"title":"Evaluation of the Imbalance Evolution in Parallel Reservoir Simulation","authors":"M. Rogowski, Suha N. Kayum","doi":"10.1109/HPEC.2019.8916495","DOIUrl":"https://doi.org/10.1109/HPEC.2019.8916495","url":null,"abstract":"Load balancing is a crucial factor affecting the performance of parallel applications. Improper work distribution leads to underutilization of computing resources and an unnecessary increase in runtime. This paper identifies the imbalance sources in reservoir simulation and characterizes them as static or dynamic. Simulation model properties that change over time, such as well management actions, are registered and correlated with performance characteristics hence identifying sources of imbalance. The results are exploratory and used to validate the current approach of static grid-to-process, and well-to-process assignment widely used in commercial parallel reservoir simulators. Areas in which implementing dynamic load balancing would be worthwhile are identified.","PeriodicalId":184253,"journal":{"name":"2019 IEEE High Performance Extreme Computing Conference (HPEC)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121806033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/HPEC.2019.8916476
Dominik Tödling, Martin Winter, M. Steinberger
Breadth-First Search is an important basis for many different graph-based algorithms with applications ranging from peer-to-peer networking to garbage collection. However, the performance of different approaches depends strongly on the type of graph. In this paper, we present an efficient algorithm that performs well on a variety of different graphs. As part of this, we look into utilizing dynamic parallelism in order to both reduce overhead from latency between the CPU and GPU, as well as speed up the algorithm itself. Lastly, integrate the algorithm with the faimGraph framework for dynamic graphs and examine the relative performance to a Compressed-Sparse-Row data structure. We show that our algorithm can be well adapted to the dynamic setting and outperforms another competing dynamic graph framework on our test set.
{"title":"Breadth-First Search on Dynamic Graphs using Dynamic Parallelism on the GPU","authors":"Dominik Tödling, Martin Winter, M. Steinberger","doi":"10.1109/HPEC.2019.8916476","DOIUrl":"https://doi.org/10.1109/HPEC.2019.8916476","url":null,"abstract":"Breadth-First Search is an important basis for many different graph-based algorithms with applications ranging from peer-to-peer networking to garbage collection. However, the performance of different approaches depends strongly on the type of graph. In this paper, we present an efficient algorithm that performs well on a variety of different graphs. As part of this, we look into utilizing dynamic parallelism in order to both reduce overhead from latency between the CPU and GPU, as well as speed up the algorithm itself. Lastly, integrate the algorithm with the faimGraph framework for dynamic graphs and examine the relative performance to a Compressed-Sparse-Row data structure. We show that our algorithm can be well adapted to the dynamic setting and outperforms another competing dynamic graph framework on our test set.","PeriodicalId":184253,"journal":{"name":"2019 IEEE High Performance Extreme Computing Conference (HPEC)","volume":"34 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117278166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/HPEC.2019.8916550
T. Davis, M. Aznaveh, Scott P. Kolodziej
SuiteSparse:GraphBLAS is a full implementation of the GraphBLAS standard, which provides a powerful and expressive framework for creating graph algorithms based on the elegant mathematics of sparse matrix operations on a semiring. Algorithms written in GraphBLAS achieve high performance with minimal development time. Using GraphBLAS, it took a mere 20 minutes to write a first-cut computational kernel that solves the Sparse Deep Neural Network Graph Challenge. Understanding the problem description and file format, writing code to read in the files that define the problem, and comparing our results with the reference solution took a full day. The kernel consists of a single for-loop around 4 lines of code, all of which are calls to GraphBLAS, and it worked perfectly the first time it was compiled. The sequential performance of the GraphBLAS solution is 3x to 5x faster than the MATLAB reference implementation. OpenMP parallelism gives an additional 10x to 15x speedup on a 20-core Intel processor, 17x on an IBM Power8 system, and 20x on a Power9 system, for the largest problems. Since SuiteSparse:GraphBLAS does not yet employ MPI, this was added at the application level, a development effort that took one week, primarily because of difficulties in resolving a load-balancing issue in the MPI-based parallel algorithm.
{"title":"Write Quick, Run Fast: Sparse Deep Neural Network in 20 Minutes of Development Time via SuiteSparse:GraphBLAS","authors":"T. Davis, M. Aznaveh, Scott P. Kolodziej","doi":"10.1109/HPEC.2019.8916550","DOIUrl":"https://doi.org/10.1109/HPEC.2019.8916550","url":null,"abstract":"SuiteSparse:GraphBLAS is a full implementation of the GraphBLAS standard, which provides a powerful and expressive framework for creating graph algorithms based on the elegant mathematics of sparse matrix operations on a semiring. Algorithms written in GraphBLAS achieve high performance with minimal development time. Using GraphBLAS, it took a mere 20 minutes to write a first-cut computational kernel that solves the Sparse Deep Neural Network Graph Challenge. Understanding the problem description and file format, writing code to read in the files that define the problem, and comparing our results with the reference solution took a full day. The kernel consists of a single for-loop around 4 lines of code, all of which are calls to GraphBLAS, and it worked perfectly the first time it was compiled. The sequential performance of the GraphBLAS solution is 3x to 5x faster than the MATLAB reference implementation. OpenMP parallelism gives an additional 10x to 15x speedup on a 20-core Intel processor, 17x on an IBM Power8 system, and 20x on a Power9 system, for the largest problems. Since SuiteSparse:GraphBLAS does not yet employ MPI, this was added at the application level, a development effort that took one week, primarily because of difficulties in resolving a load-balancing issue in the MPI-based parallel algorithm.","PeriodicalId":184253,"journal":{"name":"2019 IEEE High Performance Extreme Computing Conference (HPEC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115479197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/HPEC.2019.8916237
Mohammad Farhadi, Mehdi Ghasemi, Yezhou Yang
Nowadays most research in visual recognition using Convolutional Neural Networks (CNNs) follows the “deeper model with deeper confidence” belief to gain a higher recognition accuracy. At the same time, deeper model brings heavier computation. On the other hand, for a large chunk of recognition challenges, a system can classify images correctly using simple models or so-called shallow networks. Moreover, the implementation of CNNs faces with the size, weight, and energy constraints on the embedded devices. In this paper, we implement the adaptive switching between shallow and deep networks to reach the highest throughput on a resource-constrained MPSoC with CPU and FPGA. To this end, we develop and present a novel architecture for the CNNs where a gate makes the decision whether using the deeper model is beneficial or not. Due to resource limitation on FPGA, the idea of partial reconfiguration has been used to accommodate deep CNNs on the FPGA resources. We report experimental results on CIFAR-10, CIFAR-100, and SVHN datasets to validate our approach. Using confidence metric as the decision making factor, only 69.8%, 71.8%, and 43.8% of the computation in the deepest network is done for CIFAR10, CIFAR-100, and SVHN while it can maintain the desired accuracy with the throughput of around 400 images per second for SVHN dataset. https://github.com/mfarhadi/AHCNN.
{"title":"A Novel Design of Adaptive and Hierarchical Convolutional Neural Networks using Partial Reconfiguration on FPGA","authors":"Mohammad Farhadi, Mehdi Ghasemi, Yezhou Yang","doi":"10.1109/HPEC.2019.8916237","DOIUrl":"https://doi.org/10.1109/HPEC.2019.8916237","url":null,"abstract":"Nowadays most research in visual recognition using Convolutional Neural Networks (CNNs) follows the “deeper model with deeper confidence” belief to gain a higher recognition accuracy. At the same time, deeper model brings heavier computation. On the other hand, for a large chunk of recognition challenges, a system can classify images correctly using simple models or so-called shallow networks. Moreover, the implementation of CNNs faces with the size, weight, and energy constraints on the embedded devices. In this paper, we implement the adaptive switching between shallow and deep networks to reach the highest throughput on a resource-constrained MPSoC with CPU and FPGA. To this end, we develop and present a novel architecture for the CNNs where a gate makes the decision whether using the deeper model is beneficial or not. Due to resource limitation on FPGA, the idea of partial reconfiguration has been used to accommodate deep CNNs on the FPGA resources. We report experimental results on CIFAR-10, CIFAR-100, and SVHN datasets to validate our approach. Using confidence metric as the decision making factor, only 69.8%, 71.8%, and 43.8% of the computation in the deepest network is done for CIFAR10, CIFAR-100, and SVHN while it can maintain the desired accuracy with the throughput of around 400 images per second for SVHN dataset. https://github.com/mfarhadi/AHCNN.","PeriodicalId":184253,"journal":{"name":"2019 IEEE High Performance Extreme Computing Conference (HPEC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121533225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-09-01DOI: 10.1109/HPEC.2019.8916521
D. Ricke, James Watkins, Philip Fremont-Smith, Adam Michaleas
Massively parallel sequencing (MPS) of large single nucleotide polymorphism (SNP) panels enables identification, analysis of complex DNA mixture samples, and extended kinship predictions. Computational challenges related to SNP allele calling, probability of random man not excluded calculations, and both reference and complex mixture sample comparisons to tens of millions of reference profiles were encountered and resolved when scaling up from thousands to tens of thousands of SNP loci. A MPS SNP analysis pipeline is described for rapid analysis of forensic deoxyribonucleic acid (DNA) samples for thousands to tens of thousands of SNP loci against tens of millions of reference profiles. This pipeline is part of the MIT Lincoln Laboratory (MITLL) IdPrism advanced DNA forensic system.
{"title":"IdPrism: Rapid Analysis of Forensic DNA Samples Using MPS SNP Profiles","authors":"D. Ricke, James Watkins, Philip Fremont-Smith, Adam Michaleas","doi":"10.1109/HPEC.2019.8916521","DOIUrl":"https://doi.org/10.1109/HPEC.2019.8916521","url":null,"abstract":"Massively parallel sequencing (MPS) of large single nucleotide polymorphism (SNP) panels enables identification, analysis of complex DNA mixture samples, and extended kinship predictions. Computational challenges related to SNP allele calling, probability of random man not excluded calculations, and both reference and complex mixture sample comparisons to tens of millions of reference profiles were encountered and resolved when scaling up from thousands to tens of thousands of SNP loci. A MPS SNP analysis pipeline is described for rapid analysis of forensic deoxyribonucleic acid (DNA) samples for thousands to tens of thousands of SNP loci against tens of millions of reference profiles. This pipeline is part of the MIT Lincoln Laboratory (MITLL) IdPrism advanced DNA forensic system.","PeriodicalId":184253,"journal":{"name":"2019 IEEE High Performance Extreme Computing Conference (HPEC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127927347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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