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PRACE Seasonal School on Bioinformatics

Apr 23 – 26, 2018
SAS campus
Europe/Bratislava timezone

Contribution List

18 out of 18 displayed
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  1. 0. Opening
    Dr Lukáš Demovič (Computing Center of the SAS)
    4/23/18, 9:00 AM
  2. 1. Introduction to bioinformatics 1/2
    Prof. Erik Bongcam Rudloff (Swedish University of Agricultural Sciences)
    4/23/18, 9:30 AM
  3. 2. Introduction to bioinformatics 2/2
    Prof. Erik Bongcam Rudloff (Swedish University of Agricultural Sciences)
    4/23/18, 11:00 AM
  4. 5. Parallel programming with R (1/2)
    Dr Sirkiä Seija (Centre fo Scientific Computing - CSC, Finland)
    4/23/18, 1:30 PM
    This lecture is aimed at R users with very limited or no experience in parallel computing. You will learn how and when taking advantage of parallel computing can help you run your R scripts in less time, when not, and how to tell the difference. More importantly, you will get an idea of how to approach parallelising your task in practice. We will consider Intel Math kernel library (MKL)...
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  5. 6. Parallel programming with R (2/2)
    Sirkiä Seija (Centre fo Scientific Computing - CSC, Finland)
    4/23/18, 3:45 PM
    This lecture is aimed at R users with very limited or no experience in parallel computing. You will learn how and when taking advantage of parallel computing can help you run your R scripts in less time, when not, and how to tell the difference. More importantly, you will get an idea of how to approach parallelising your task in practice. We will consider Intel Math kernel library (MKL)...
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  6. 7. Analysing large datasets with Apache Spark (1/8)
    Mr Apurva Nandan (Centre fo Scientific Computing - CSC, Finland)
    4/24/18, 9:00 AM
    With the rapid growth in data volume that is being used in data analysis tasks, it gets more and more challenging for the user to process it using standard methods. Enter Spark, a high-performance distributed computing framework, which allows us to tackle big-data problems by distributing the workload across a cluster of machines. This two day course discusses the advantage of cloud computing...
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  7. 8. Analysing large datasets with Apache Spark (2/8)
    Dr Apurva Nandan (Centre fo Scientific Computing - CSC, Finland)
    4/24/18, 11:00 AM
    With the rapid growth in data volume that is being used in data analysis tasks, it gets more and more challenging for the user to process it using standard methods. Enter Spark, a high-performance distributed computing framework, which allows us to tackle big-data problems by distributing the workload across a cluster of machines. This two day course discusses the advantage of cloud computing...
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  8. 9. Analysing large datasets with Apache Spark (3/8)
    Dr Apurva Nandan (Centre fo Scientific Computing - CSC, Finland)
    4/24/18, 2:00 PM
    With the rapid growth in data volume that is being used in data analysis tasks, it gets more and more challenging for the user to process it using standard methods. Enter Spark, a high-performance distributed computing framework, which allows us to tackle big-data problems by distributing the workload across a cluster of machines. This two day course discusses the advantage of cloud computing...
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  9. 10. Analysing large datasets with Apache Spark (4/8)
    Dr Apurva Nandan (Centre fo Scientific Computing - CSC, Finland)
    4/24/18, 4:00 PM
    With the rapid growth in data volume that is being used in data analysis tasks, it gets more and more challenging for the user to process it using standard methods. Enter Spark, a high-performance distributed computing framework, which allows us to tackle big-data problems by distributing the workload across a cluster of machines. This two day course discusses the advantage of cloud computing...
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  10. 11. Analysing large datasets with Apache Spark (5/8)
    Dr Apurva Nandan (Centre fo Scientific Computing - CSC, Finland)
    4/25/18, 9:00 AM
    With the rapid growth in data volume that is being used in data analysis tasks, it gets more and more challenging for the user to process it using standard methods. Enter Spark, a high-performance distributed computing framework, which allows us to tackle big-data problems by distributing the workload across a cluster of machines. This two day course discusses the advantage of cloud computing...
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  11. 12. Analysing large datasets with Apache Spark (6/8)
    Dr Apurva Nandan (Centre fo Scientific Computing - CSC, Finland)
    4/25/18, 11:00 AM
    With the rapid growth in data volume that is being used in data analysis tasks, it gets more and more challenging for the user to process it using standard methods. Enter Spark, a high-performance distributed computing framework, which allows us to tackle big-data problems by distributing the workload across a cluster of machines. This two day course discusses the advantage of cloud computing...
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  12. 13. Analysing large datasets with Apache Spark (7/8)
    Dr Apurva Nandan (Centre fo Scientific Computing - CSC, Finland)
    4/25/18, 2:00 PM
    With the rapid growth in data volume that is being used in data analysis tasks, it gets more and more challenging for the user to process it using standard methods. Enter Spark, a high-performance distributed computing framework, which allows us to tackle big-data problems by distributing the workload across a cluster of machines. This two day course discusses the advantage of cloud computing...
    Go to contribution page
  13. 14. Analysing large datasets with Apache Spark (8/8)
    Dr Apurva Nandan (Centre fo Scientific Computing - CSC, Finland)
    4/25/18, 4:00 PM
    With the rapid growth in data volume that is being used in data analysis tasks, it gets more and more challenging for the user to process it using standard methods. Enter Spark, a high-performance distributed computing framework, which allows us to tackle big-data problems by distributing the workload across a cluster of machines. This two day course discusses the advantage of cloud computing...
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  14. 15. BLAST (1/2)
    Dr Kimmo Mattila (Centre fo Scientific Computing - CSC, Finland)
    4/26/18, 9:00 AM
    Running BLAST in Clusters NCBI BLAST is one of the most of the most frequently used bioinformatics tools. BLAST answers to the question: “What known sequences are significantly similar to my sample sequence”. Answers to this question is needed in numerous bioinformatics analyses and work-flows. As the sequence databases keep growing as well as the sizes of the data sets to be analyzed, a HPC...
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  15. 16. BLAST (2/2)
    Dr Kimmo Mattila (Centre fo Scientific Computing - CSC, Finland)
    4/26/18, 11:00 AM
    Running BLAST in Clusters NCBI BLAST is one of the most of the most frequently used bioinformatics tools. BLAST answers to the question: “What known sequences are significantly similar to my sample sequence”. Answers to this question is needed in numerous bioinformatics analyses and work-flows. As the sequence databases keep growing as well as the sizes of the data sets to be analyzed, a HPC...
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  16. 17. Closing remarks
    Dr Lukáš Demovič (Computing Center of the SAS)
    4/26/18, 12:40 PM
  17. 4. Hands-on session
  18. 3. Overview