71 research outputs found

    Automatically Harnessing Sparse Acceleration

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    Sparse linear algebra is central to many scientific programs, yet compilers fail to optimize it well. High-performance libraries are available, but adoption costs are significant. Moreover, libraries tie programs into vendor-specific software and hardware ecosystems, creating non-portable code. In this paper, we develop a new approach based on our specification Language for implementers of Linear Algebra Computations (LiLAC). Rather than requiring the application developer to (re)write every program for a given library, the burden is shifted to a one-off description by the library implementer. The LiLAC-enabled compiler uses this to insert appropriate library routines without source code changes. LiLAC provides automatic data marshaling, maintaining state between calls and minimizing data transfers. Appropriate places for library insertion are detected in compiler intermediate representation, independent of source languages. We evaluated on large-scale scientific applications written in FORTRAN; standard C/C++ and FORTRAN benchmarks; and C++ graph analytics kernels. Across heterogeneous platforms, applications and data sets we show speedups of 1.1×\times to over 10×\times without user intervention.Comment: Accepted to CC 202

    Clinical pregenetic screening for stroke monogenic diseases: Results from lombardia GENS registry

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    BACKGROUND AND PURPOSE: Lombardia GENS is a multicentre prospective study aimed at diagnosing 5 single-gene disorders associated with stroke (cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy, Fabry disease, MELAS [mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes], hereditary cerebral amyloid angiopathy, and Marfan syndrome) by applying diagnostic algorithms specific for each clinically suspected disease METHODS: We enrolled a consecutive series of patients with ischemic or hemorrhagic stroke or transient ischemic attack admitted in stroke units in the Lombardia region participating in the project. Patients were defined as probable when presenting with stroke or transient ischemic attack of unknown etiopathogenic causes, or in the presence of <3 conventional vascular risk factors or young age at onset, or positive familial history or of specific clinical features. Patients fulfilling diagnostic algorithms specific for each monogenic disease (suspected) were referred for genetic analysis. RESULTS: In 209 patients (57.4\ub114.7 years), the application of the disease-specific algorithm identified 227 patients with possible monogenic disease. Genetic testing identified pathogenic mutations in 7% of these cases. Familial history of stroke was the only significant specific feature that distinguished mutated patients from nonmutated ones. The presence of cerebrovascular risk factors did not exclude a genetic disease. CONCLUSIONS: In patients prescreened using a clinical algorithm for monogenic disorders, we identified monogenic causes of events in 7% of patients in comparison to the 1% to 5% prevalence reported in previous series

    A comprehensive assessment of somatic mutation detection in cancer using whole-genome sequencing.

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    As whole-genome sequencing for cancer genome analysis becomes a clinical tool, a full understanding of the variables affecting sequencing analysis output is required. Here using tumour-normal sample pairs from two different types of cancer, chronic lymphocytic leukaemia and medulloblastoma, we conduct a benchmarking exercise within the context of the International Cancer Genome Consortium. We compare sequencing methods, analysis pipelines and validation methods. We show that using PCR-free methods and increasing sequencing depth to ∼ 100 × shows benefits, as long as the tumour:control coverage ratio remains balanced. We observe widely varying mutation call rates and low concordance among analysis pipelines, reflecting the artefact-prone nature of the raw data and lack of standards for dealing with the artefacts. However, we show that, using the benchmark mutation set we have created, many issues are in fact easy to remedy and have an immediate positive impact on mutation detection accuracy.We thank the DKFZ Genomics and Proteomics Core Facility and the OICR Genome Technologies Platform for provision of sequencing services. Financial support was provided by the consortium projects READNA under grant agreement FP7 Health-F4-2008-201418, ESGI under grant agreement 262055, GEUVADIS under grant agreement 261123 of the European Commission Framework Programme 7, ICGC-CLL through the Spanish Ministry of Science and Innovation (MICINN), the Instituto de Salud Carlos III (ISCIII) and the Generalitat de Catalunya. Additional financial support was provided by the PedBrain Tumor Project contributing to the International Cancer Genome Consortium, funded by German Cancer Aid (109252) and by the German Federal Ministry of Education and Research (BMBF, grants #01KU1201A, MedSys #0315416C and NGFNplus #01GS0883; the Ontario Institute for Cancer Research to PCB and JDM through funding provided by the Government of Ontario, Ministry of Research and Innovation; Genome Canada; the Canada Foundation for Innovation and Prostate Cancer Canada with funding from the Movember Foundation (PCB). PCB was also supported by a Terry Fox Research Institute New Investigator Award, a CIHR New Investigator Award and a Genome Canada Large-Scale Applied Project Contract. The Synergie Lyon Cancer platform has received support from the French National Institute of Cancer (INCa) and from the ABS4NGS ANR project (ANR-11-BINF-0001-06). The ICGC RIKEN study was supported partially by RIKEN President’s Fund 2011, and the supercomputing resource for the RIKEN study was provided by the Human Genome Center, University of Tokyo. MDE, LB, AGL and CLA were supported by Cancer Research UK, the University of Cambridge and Hutchison-Whampoa Limited. SD is supported by the Torres Quevedo subprogram (MI CINN) under grant agreement PTQ-12-05391. EH is supported by the Research Council of Norway under grant agreements 221580 and 218241 and by the Norwegian Cancer Society under grant agreement 71220-PR-2006-0433. Very special thanks go to Jennifer Jennings for administrating the activity of the ICGC Verification Working Group and Anna Borrell for administrative support.This is the final version of the article. It first appeared from Nature Publishing Group via http://dx.doi.org/10.1038/ncomms1000
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