1. Using Pattern-Models to Guide SSD Deployment for Big Data in HPC Systems Junjie Chen 1, Philip C. Roth 2, Yong Chen 1 1 Data-Intensive Scalable Computing Lab (DISCL) Department of Computer Science Texas Tech University 2 Oak Ridge National Laboratory 1

2. Background HPC applications are increasingly data-intensive Scientific simulations have already reached 100TB – 1PB of data volume, projected at the scale of 10PB – 100PB for upcoming exascale Collected data from instruments increases rapidly too, in a global climate model, with 100 × 120 km grid cell, PBs of data managed Such trend brings a critical challenge Efficient I/O access demands Highly efficient storage system 2

3. Over 90% of all data in the world is being stored on magnetic media (hard disk drives, HDDs) IBM invented in 1956 Mechanism remains the same since then Various mechanical moving parts High latency, slow random access performance, unreliable, power hungry Large capacity, low cost (USD 0.10/GB), impressive sequential access performance 3 Storage Media

4. Non-volatile storage-class memory (SCM) Flash-memory based Solid State Drives (SSDs), PCRAM, NRAM, … Use microchips which retain data in non- volatile memory (array of floating gate transistors isolated by an insulating layer) Superior performance, high bandwidth, low latency (esp. random accesses), less susceptible to physical shock, power efficient Low capacity, high cost (USD 0.90-2/GB), block erasure, wear out (10K-100K P/E cycles) Intel® X25-E SSD 4 Emerging Storage Media

5. Motivation The challenge of leveraging SSDs and maximizing benefits remains daunting. Deploy SSDs on different nodes can have different impacts The fixed hardware budget needs to be considered. A cost-effective decision of deployment needs to be made at design/deployment phase of HPC systems. 5 Interconnect Local SSD storage

6. Our Study To investigate different deployment strategies Compute side and storage side Characteristics of SSDs, ratios, access patterns Consider a fixed hardware budget Pattern-Model Guided Deployment Approach Considering I/O access pattern of workloads Considering SSD characteristics via a performance model 6

7. Our Contributions We propose a pattern-model guided deployment approach We introduce a performance model to quantitatively analyze different SSD deployment strategies We try to answer the questions of how SSDs show be utilized for big data applications in HPC systems We have carried out initial experimental tests to verify the proposed approach. 7

8. Pattern-Model Guided Approach 8 Workloads I/O Requests Operation Types Workload Size Spatial Pattern Workload Characterization Pattern-Model Guided Approach Storage Arrays Parallel File System Storage Configuration HDDs SSDs Analytical Model Strategy Mapping

9. Workload I/O Access Pattern 9 Workload Characterization Request size, I/O operation type, spatial pattern, and ratio of local requests to remote requests. Given or obtained from I/O characterization tools, like Darshan and IOSIG. Strategy Mapping Analysis i -> Strategy j For a specific pattern, give a specific deployment strategy.

10. Performance Model 10 R is the total response time, R local is the local response time, R remote is the remote response time and R inter is the time spent on interconnection. W is the workload and B is the aggregate bandwidth.

11. Performance Model (cont.) 11 We characterize the three different response time respectively and estimate the total response time L ssd Latency of SSD L hdd Latency of HDD γ Percentage of workload serviced locally ω The available capacity of SSDs p Percentage of SSD budget deployed on compute nodes

12. Performance Model (cont.) 12 The tradeoff analysis, C is the capacity of SSD which one compute node could utilize, G is the SSD budget, and n is the number of compute nodes. Compute-side deployment: all the SSDs on compute nodes Storage-side deployment: all the SSDs on storage nodes Compute-Storage deployment: SSDs on both types of nodes

13. Preliminary Results and Analysis IOR Tested the aggregate bandwidth/execution time File size is varied, and the performance of sequential read and write, and random read and write is tested. MPI-IO Test Tested the aggregate bandwidth/execution time With different file sizes and operation types (sequential read and write, random read and write). Both benchmark with ratio γ= ¼. 13

14. Preliminary Results and Analysis (cont.) 14 IOR

15. Preliminary Results and Analysis (cont.) 15 MPI-IO Test

16. Conclusion Flash-memory based SSDs are promising storage devices in the storage hierarchy of HPC systems. Different deployment strategies of SSDs can impact the performance given a fixed hardware budget We proposed a pattern-model guided approach Model the performance impact of various deployment strategies Considering workload characterization and device characteristics Mapping to deployment strategy This study provides a possible solution that guides such placement and deployment strategies 16

17. Ongoing and Future Work A Unified HPC Storage System Managing Heterogeneous Devices We study the needs of a well- managed and unified heterogeneous storage system for HPC workloads We propose a working-set based reorganization scheme (WS-ROS) Explore the capability of SSDs and HDDs Provide a highly efficient storage system for HPC workloads 17

18. ACKNOWLDEGEMENT: This research is sponsored in part by the Advanced Scientific Computing Research program, Office of Science, U.S. Department of Energy. This research is also sponsored in part by Texas Tech University startup grant and National Science Foundation under NSF grant CNS-1162488. The work was performed in part at the Oak Ridge National Laboratory, which is managed by UT-Battelle, LLC under Contract No. De-AC05-00OR22725. Accordingly, the U.S. Government retains a non-exclusive, royalty-free license to publish or reproduce the published form of this contribution, or allow others to do so, for U.S. Government purposes. Thank You Please visit our website: http://discl.cs.ttu.eduhttp://discl.cs.ttu.edu 18 Q&A