HPC 2.0 for Genomics. An Introduction to IBM HPDA Framework & Reference Architecture. Frank Lee, PhD IBM Systems
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1 HPC 2.0 for Genomics An Introduction to IBM HPDA Framework & Reference Architecture Frank Lee, PhD IBM Systems
2 IBM Systems Builds the Foundation for the Cognitive Era Sample Industry Partners Clinical Informatics Genomic Analysis Image Analysis Deep Learning Applications & Frameworks Data Repositories and Databases WORKLOAD ORCHESTRATION IBM Spectrum Computing Software Defined Infrastructure Compute & Storage Servers Flash Disk Optimize utilization of compute resources across the enterprise ENTERPRISE DATA MANAGEMENT IBM Spectrum Scale Improve data access and optimize storage utilization across the enterprise VM Tape x86 POWER IT Administration 2
3 Foundation for Data Clinical Informatics Genomic Analysis Image Analysis Deep Learning Applications & Frameworks Software Defined Infrastructure WORKLOAD ORCHESTRATION IBM Spectrum Computing ENTERPRISE DATA MANAGEMENT IBM Spectrum Scale Compute & Storage Servers Flash Disk Tape x86 POWER VM
4 Challenge 1: High Speed for Big Data Next Generation Sequencing (NGS) Raw Data: Up to 200 GB/file (compressed) Processed Variant Data: Up to 500 MB/file Biomedical Imaging Medical Imaging: MRI, CT, Ultrasound,... Microscopy Time Varying Sensors Medical Monitors Personal Sensors Super speed SSD Flash Super capacity policy engine for ingesting Curated Scientific Literature Text files: CSV, TXT Online Web Crawls
5 Client Reference Before After 50 hours using 1 Node ~24cores, 1 QDR link, 256GB RAM 5 hours using 1 Node ~12cores, 1 FDR Link, 64GB RAM
6 Data Machine for HPC 2.0 Capability U 0.5PB 4GB/s 8 10X U 5PB 34GB/s Fault Tolerance 2014 HW RAID Rebuild: weeks MTTDL: 1 week (4+P) >1000X 2017 SW RAID Rebuild: minutes MTTDL: 200M Y (8+3P) MTTDL: for 50,000 disk
7 Challenge 2: Data Sharing for Global Collaboration Policy Engine For Sharing C: /data HDFS HTTP S3 A data scientist anywhere in the world can access the most nearby resource for data & compute Share Data Anywhere AFM
8 Challenge 3: Cost Control policy engine: tiering, compression Store Data Anywhere Local Tier Cloud Tier Genomic data doubling 6 12 months But budgets are not! PB 170PB unlimited
9 Client Reference: Scale Cost Performance 10X Better performance on the same hardware Cost 90% Reduction of storage cost
10 Client References Cont d: Scale Cost 4/11/
11 Foundation for Workload Clinical Informatics Genomic Analysis Image Analysis Deep Learning Applications & Frameworks Software Defined Infrastructure WORKLOAD ORCHESTRATION IBM Spectrum Computing ENTERPRISE DATA MANAGEMENT IBM Spectrum Scale Compute & Storage Servers Flash Disk Tape x86 POWER VM
12 Challenge 4: Workflow Optimization Example #1: Resource Utilization for Workflow Reduce processing times time time x86 POWER VM 4/11/
13 Client References: Workflow Optimization Data-aware scheduling with API IO-aware scheduling with real-time data IO-aware scheduling with some math github/stjude 4/11/
14 Challenge 5 Workflow Automation Example #1: Genomic Analysis Pipelines Whole Human coverage Sub flow module FastQ Processing time per genome High Throughput Sequencing Assembly & Alignment ~ 150 GB (compressed) SAM / BAM ~ 100 GB Job arrays 1 to 100 hours * on 1 compute node Variant Calling VCF 100 to 200 MB Variant Annotations Annotated VCF 500 MB error handling Provides logic syntax
15 Client Reference: Workflow Automation 15
16 Foundation for Metadata & Provenance Clinical Informatics Genomic Analysis Image Analysis Deep Learning Applications & Frameworks Software Defined Infrastructure WORKLOAD ORCHESTRATION IBM Spectrum Computing ENTERPRISE DATA MANAGEMENT IBM Spectrum Scale Compute & Storage Servers Flash Disk Tape x86 POWER VM
17 Challenge 6: Provenance File name File size Filer owner File path File set name File permission File ctime File atime File mtime What When Cluster name Global file ID Job submission user Job ID Job name Flow ID (Parsed from job name) Job status Job start time Job finish time Job submission cmd Job working directory Input files User variables Cluster name Server name and port Global file ID Flow owner Flow ID Flow definition name Flow definition version Flow status Flow start time Flow finish time Flow working directory Input files User variables Filename = MDA1.vcf Fileid = ProjectID = AXVCBS FlowID = RefGenome = V38 UserV1 = EXP1 ActionTag = NeverMove
18 Beyond Provenance METADATA driven Cognitive Engine What When How Why
19 Foundation for Cloud Clinical Informatics Genomic Analysis Image Analysis Deep Learning Applications & Frameworks Software Defined Infrastructure WORKLOAD ORCHESTRATION IBM Spectrum Computing ENTERPRISE DATA MANAGEMENT IBM Spectrum Scale Compute & Storage Servers Flash Disk Tape x86 POWER VM
20 A Hybrid Cloud Architecture On-premise infrastructure Cloud infrastructure Workloads On-Premise Cluster Spectrum LSF Secure VPN tunnel Cloud Resident Cluster Spectrum Scale (GPFS) IBM Aspera FASP Spectrum Scale Spectrum Scale (GPFS) Transparent Cloud Tiering IBM Elastic Storage IBM Elastic Storage AFM = Active File Management 20
21 Workflow in Multi cloud ReplicateTo Remote Site B Site A Site C
22 Application level Optimization Sample Industry Partners Clinical Informatics Genomic Analysis Image Analysis Deep Learning Applications & Frameworks Data Repositories and Databases Software Defined Infrastructure Compute & Storage Servers Flash Disk WORKLOAD ORCHESTRATION IBM Spectrum Computing ENTERPRISE DATA MANAGEMENT IBM Spectrum Scale VM Tape x86 POWER IT Administration 22
23 Application level Optimization IBM POWER continues to develop technologies that accelerate compute for the next generation of analytics, including the latest deep learning & machine learning algorithms Sample POWER based Technology: NVLink Nvidia GPU with NVLink IBM POWER S822LC Basic Advantages over Intel Haswell GPU Memory 720 GB/s NVLink 80 GB/s 720 GB/s GPU Memory Feature Intel Haswell IBM POWER8 SMT / Core 2 Threads 8 Threads L1d Cache / Core 32 KB 64 KB L2 Cache / Core 256 KB 512 KB System Memory 115 GB/s Power S822LC CPU L3 Cache / Processor 16 to 45 MB 80 to 96 MB L4 Cache / System 64 MB to 2 GB Maximum Sustained Memory Bandwidth 53 GB/s 224 GB/s Up to 4 integrated Nvidia P100 Pascal GPUs Delivering > 2.5X the bandwidth to GPUs Reduces common CPU GPU bottlenecks 23
24 Open Source Genomics Applications Optimized with POWER8 ALLPATHS LG BarraCUDA bamtools Bedtools Bfast Bioconductor BioPerl BioPython BLAST (NCBI) Bowtie Bowtie2 BreakDancer BWA Chimerascan Conda ClustalW Cufflinks DELLY2 EMBOSS FASTA FastQC FASTX Toolkit FreeBayes GenomicConsensus GenomeFisher GraphViz HMMER HTSeq Htslib IGV InterProScan ISAAC3 irods Mothur MrBayes MrBayes5d MUSCLE Numpy Pandas PHYLIP PICARD Pindel PLINK PRADA Pysam Python R RNAStar/STAR RSEM SAMTools Sailfish Scalpel SHRiMP SIFT Snpeff SOAP3 DP SOAPaligner SOAPdenovo SoapFuse SQLite Sratoolkit STAR fusion Tabix Tablet Tassel T Coffee TMAP TopHat TranSMART Trinity Variant_tools Varscan Velvet/Oases bamkit bedops cutadapt diamond kraken lumpy parallel PLINK2 primer3 QIIME R cowplot R tidyverse Turn key: Pre built binaries and complete build scripts Optimized: POWER8 binaries Salmon Samblaster Scikit bio Seqtk Spades Trimmonmatic Vcftools Long Term Support: Community sponsorship and support contracts ensure ongoing support for tools IBM Systems
25 Qatar Genome Project Industry Partners Clinical Informatics Genomic Analysis Image Analysis Deep Learning Applications & Frameworks Data Repositories and Databases Software Defined Infrastructure WORKLOAD ORCHESTRATION IBM Spectrum Computing ENTERPRISE DATA MANAGEMENT IBM Spectrum Scale IT Administration
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