SW and HW System Architecture For Big Data CHPC 2013 Conference Cape Town, SA

Transcription

1 SW and HW System Architecture For Big Data CHPC 2013 Conference Cape Town, SA Giri Chukkapalli, Ph.D Principal Engineer, CTO Office, William Blake, CTO, Cray Inc Dec 05, 2013

2 Topics Whats all the fuss about Big Data? Big Data Drivers Big Data Application characteristics Big Data in HPC Big Data SW infrastructure Big Data HW infrastructure Cray s efforts in Big Data

3 Enterprise Computing: Small Data Enterprise Computing traditionally relied on Relational Databases (RDBMS) Payroll, ERP, CRM etc. Deals with structured, tabular data Linear or sub-linear data growth Require strict ACID properties Vertically scaling infrastructure CAP constraint prevents horizontal scaling Sharding Examples: Oracle, IBM, SAP business solutions Large scale SMPs, Mainframes Largely Human Generated ACID: Atomicity, Consistency, Isolation, Durability CAP: Consistency, Availability, Partition Tolerance

4 Big Data Push Growth of unstructured data Textual, Geospatial, temporal logs, audio-visual, sensor data Internet services data Search Engines, Social networking, GPS, Security Growth of Unstructured Computations Graph Analytics, Business Intelligence (BI), classification, clustering Big Data is loosely defined as Data as well as computing that is not amenable to traditional RDBMS infrastructure This definition is the partial reason for the confusion and hype around Big Data Largely Non-Human generated and increasingly Consumed by Human Proxies Not All Big Data problems are same and may require widely disparate infrastructure

5 Big Data Drivers Reason for the exponential growth of Big Data Surprisingly Moore s Law that is behind the growth of HPC is also responsible for Big Data Sensors once only found on space shuttle now available on most mobile devices and increasingly propagating into every day things ( Internet of things ) ~250 sensors per person within next 5 years Resolution and data generation capability of these devices increasing with time Consumers of this data are growing with time This is also the reason Why We should care about Big Data in HPC and Scientific domains Highly nonlinear multi-scale, multi-physics simulations will be increasingly driven/steered by Big Data At the infrastructure level, there is increasing convergence between traditional HPC and Enterprise Big Data /Cloud requirements

6 Big Data and Why We Should Care Big Data refers to data that is not easily captured, managed and analyzed by traditional tools due to: Volume (growing > 60%/yr), Velocity (often real time streaming), Variety (all forms of unstructured data: logs, docs, images) IDC expects Hadoop, an enabling technology, to run on over 50% of Big Data Projects over time representing a $8.5B market by 2015 Science will increasingly be (sensor) data-driven to understand the world Business will increasingly be data-driven to understand customers

7 Big Data Means New Kinds of Data EMC estimates that by 2020 there will be 40,000 Exabytes of data created, although the majority of that data will not be created by humans but sensors Analysis Gap Able to Analyze Source: IDC White Paper sponsored by EMC May 2009

8 Interesting Trends Google Trends shows Big Data search volume recently exceeded Business Intelligence search volume while Supercomputing search volume drifts lower

9 Descending Into Gartner s Trough

10 Big Data in HPC Examples of leading Big Data drivers in HPC High Energy Physics Experiments at CERN AstroPhysics Observations at Square Kilometer Array (SKA) Human Brain Project from EU, Connectome project from US Protein folding research driven by Protein Data Bank, Genomics data banks Sequencing whole Human Microbiome Climate/weather simulations driven by observational data (4DVAR) Predictive failure analysis in various Engineering fields driven by empirical data Smart grid (Energy Grid) project in California at LLNL

11 OSTP Press Release on Big Data (Text excerpted from Full Release issued on March 29, 2012) OBAMA ADMINISTRATION UNVEILS BIG DATA INITIATIVE: ANNOUNCES $200 MILLION IN NEW R&D INVESTMENTS Aiming to make the most of the fast-growing volume of digital data, the Obama Administration today announced a Big Data Research and Development Initiative. By improving our ability to extract knowledge and insights from large and complex collections of digital data, the initiative promises to help solve some the Nation s most pressing challenges. To launch the initiative, six Federal departments and agencies today announced more than $200 million in new commitments that, together, promise to greatly improve the tools and techniques needed to access, organize, and glean discoveries from huge volumes of digital data. In the same way that past Federal investments in information-technology R&D led to dramatic advances in supercomputing and the creation of the Internet, the initiative we are launching today promises to transform our ability to use Big Data for scientific discovery, environmental and biomedical research, education, and national security, said Dr. John P. Holdren, Assistant to the President and Director of the White House Office of Science and Technology Policy. Several Open Government, Open Data initiatives around the world Bold emphasis added by presenter

12 Characterizing Big data Applications Computational requirements represented by FLOPS or IOPS far out weighted by capacity, BW or latency requirements of memory hierarchy Working data set doesn t fit in memory (Capacity limited) FPU and or IU stalled for data access (BW limited) Highly irregular access (Latency limited) On a given distributed platform, Applications are Memory bound (capacity, BW, latency), Interconnect bound (BW, Latency), or IO bound (Capacity, BW, Latency) Mirror opposite of Compute bound problems Accordingly, SW & HW infrastructure requirements are quite different

13 Bytes/s/OPS>?: Big Data 13 Coherency management strategy: Distributed Private memory with efficient messaging Global Addressable Memory (PGAS) Distributed Memory with Transactional Consistency support Distributed Cache Coherent Memory Hybrid HW/SW support for these consistency models Minimizing data movement is fundamental to future HPC System Architectures

14 Latencies are hard to improve Latency Numbers Every programmer should know: ency.html A 2.5GHz processor clock cycle is 0.4ns Memory with 60ns latency is 100+ cycles away

15 HW and SW infrastructure for Big Data

16 HPC System architecture Login Nodes 10/100 GigE. Compute Resource Compute Resource SMP MN MN OSS OSS MDS IB Fabric (Rail 1) IB Fabric (Rail 2) Compute Nodes Visualization Test System(s) Data Mover Network Lustre Enterprise Storage (SAM-QFS)

17 Big Data HW Architecture Latency tolerant, trivially parallel workloads Loosely connected clusters Sort, search etc. Instant search and other near real time problems etc. Tightly coupled MPPs Partitionable over a low latency, high speed fabric Graph analytics and other all to all dependent problems Single System Image UMA based SMPs Large NUMA SMPs to some extent

18 Big Data Fast Data Analytic Appliances SHARED MEMORY (urika Graph, SAP Hana, Exasol, RDBMS) MPP DISTRIBUTED MEMORY (Cray Cascade and Hadoop) MPP Global Memory Fast Data (unstructured) MPP MPI Interconnects Big Data ETHERNET CLUSTERS (IBM/NETEZZA, ORACLE EXA) SAN Interconnects Enterprise Data (unstructured) MPI on Ethernet Cluster CLOUD GRID LAN/WAN (structured) interconnects 18

19 Cloud, data intensive architecture InfiniBand to 10 Gb Ethernet bridges InfiniBand Fabric (120 Gbps cabling) Cloud Compute Tier-1 Sites NFS Visualization 1GbE Fan Out 1GbE Fan Out IB+10GbE Switch IB+10GbE Switch InfiniBand Switches Lustre File System MDS OSS Data-intensive Compute Tier-2 Site

20 Cray XC30 architecture

21 Cray XMT UMA architecture

22 SYSTEMS MIDDLEWARE Provisio n SLURM Launch Resourc e Mgt Fabric Mgt Portals 4.0 HPC Software stack Applications HPC Software Stack Libraries / Tools Compilers MPI PGAS Integrated System Management OFED HW LWK Kernel/OS/VM CPU with Attached Accelerator

23 Big Data SW architecture Preprocessing: Extract, Transform, Load (ETL) Highly disparate unstructured data Scalable distributed data store Relational, columnar, various NoSQL stores Computational frameworks Map-Reduce, Graph analytics Post processing Visualization Enhanced cloud stack for System middleware and Kernel/OS/VM Example Openstack ETL and Middleware are the biggest gaps in Big Data Stack

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25 Big Data Map Reduce stack Hadoop is Apache open source version of Google MapReduce Parallel framework for simple workloads with split complexity Hides work distribution and synchronization from the user Huge ecosystem developed around Hadoop Interfaces to various languages Workflows Realtime interfaces etc. Operates on Key-Value format data Comes with its own file system HDFS Highly fault tolerant and inefficient Stores intermediate results on Disk Several alternate optimized implementations Berkley AMP Lab done some of the best work Brought the HPC expertise and adopted the best of Big Data framework

26 Hadoop SW stack example

27 Hadoop Ecosystem PIG is framework on top of Hadoop Hive is SQL like Framework on top of Hadoop HBase is real time layer over Hadoop Accumulo fork of HBase with security features AVRO data serialization HCatalog is metadata manager WHIRR is system MW to cloudize Hadoop Mahout: Machine learning layer over Hadoop Crunch is higher level layer that generate hadoop jobs Giraph is memory to memory or Graph version of Hadoop

28 Cray urika Graph Analytics Appliance

29 Cray s efforts in Big Data

31 Cray s Vision: The Fusion of Supercomputing and Big & Fast Data Modeling The World Cray Supercomputers solving grand challenges in science, engineering and analytics Data Models Integration of datasets and math models for search, analysis, predictive modeling and knowledge discovery Data- Intensive Processing High throughput event processing & data capture from sensors, data feeds and instruments Math Models Modeling and simulation augmented with data to provide the highest fidelity virtual reality results Compute Store Analyze Copyright Copyright 2013 Cray 2013 Inc. Cray Inc. Sli de

32 We Build Computational Tools That Help Change The World Supercomputing Big Data Compute Supercomputers Flexible Clusters Hybrid Architectures Store Integrated Storage & Data Management Tiered Storage Archive Analyze Graph Analytics Hadoop Solutions Merging Big Data and Supercomputing C O M P U T E S T O R E A N A L Y Z E Copyright 2013 Cray Inc. 33

33 Adapting to Data-Intensive Computing: Adding Value at the Edge of the Network Data Processors x86 Many-core GPU Multi-threaded Adaptive Supercomputing Compilers, Auto-tuning Operating System High throughput scheduling, Adaptive Runtime, Network Memory Pools High Performance Memory DRAM Active NVRAM HMC The most capable interconnects will be key to analytic workloads Storage I/O Network I/O Storage SAS to PCIe Tightly coupled Software Stack for RAID Very Low Latency Intelligent I/O Protocol Load Balancing Specialized Data Ingest Policy Based Data Movement Software Defined Network Copyright 2013 Cray Inc. 34

34 Is Triple Redundancy Really the Best Way? Redundant Arrays of Inexpensive Nodes sounds cool but it is anything but cool! Google runs over 1,000,000 servers using over 260 MW! 100 searches = power to iron 1 shirt! Businesses cannot afford such inexpensive resiliency especially datacenters in urban areas Cray s MPP Hadoop approach can dramatically lower total power required for Big Data jobs! Google s Velcro Server RAIN for the Cloud! Ref: Rich Miller blog

35 Supercomputing MapReduce Enhancements, vs. Distributed Hadoop Performance Faster Data MapReduce enhancements and raw I/O for massive throughput Faster Analytics More queries and faster answers Real Time Hardware and software stack for near real-time MapReduce analytics Efficiency Fewer Disks - ~20% availability overhead, vs. 200% with Hadoop/HDFS Fewer Racks Much greater density, than Hadoop/HDFS Efficient Processing MapReduce enhancements to eliminate unnecessary processing and limit data movement TCO Less Gear to Buy- Do more with less investment in gear Easier to Manage Smaller staff, with one integrated management stack Lower Footprint & Power Increased efficiency & density, vs. distributed Copyright 2013 Cray Inc. 36

37 Concluding Comments Data deluge will continue to grow in various fields Big Data will soon move from hype to useful field Hybrid models, equations based simulations steered by Empirical data will become more common Noise to Signal ratio also grows exponentially with the data

38 Concluding Comments Warehouse scale distributed computing, aka Cloud, provides an excellent multi-tenancy resource for high throughput capacity computing Cray expects capacity workloads, that run fine on up to 500 to 1000 node Ethernet connected clusters, will increasingly migrate from cluster to cloud But highly parallel analytic workloads, especially those that require low latency messaging and/or global memory operations that benefit greatly from the high performance interconnects and tight integration of MPP machines, will not migrate from MPP to Cloud We do expect many Cloud developments to condense into future MPP systems, including programming models, software defined networks, and hypervisors that combined with the high performance message passing and global atomic memory support in networks such as the Cray Aries network will best support the fusion of HPC and large-scale analytics

39 Thank You!