October 28, 2013 Resolving High Performance Computing and Big Data Application Bottlenecks with Application-Defined Flash Acceleration 朱义普 Director, North Asia, HPC
DDN Storage Vendor for HPC & Big Data DDN is a Leader in Massively Scalable Platforms and Solutions for Big Data and Cloud Applications Established: 1998 Main Office: Sunnyvale, California, USA Employees: 550+ Worldwide Worldwide Presence: 20 Countries Installed Base: 1,000+ End Customers; 50+ Countries Go To Market: Global Partners, Resellers, Direct World-Renowned & Award-Winning: All Time Winner
DDN The HPC Leader. The Top 500 Leader 2/3 of the World s Top100 Systems The World s Fastest File System @ ORNL 1TB/s+ Industry Leading Customers In Every HPC Segment
Spotlight ORNL SpiderII 32 PB capacity (after RAID) >1TB/s aggregate performance 4 Rows of Scale-Out Infrastructure 36 x SFA12K High Performance Storage Arrays 20,160 Nearline SAS Drives 288 Lustre Object Storage Servers 4 High Performance Metadata Servers Big Data = Small & Big I/O September 2013 DDN SHPCP Technical Presentation at SEG 2013
SFX (Storage Fusion Xcelerator ) Design Paradigm Other Approaches SFX Technology Little Data Oracle, SAP, OLTP Exchange, VM Flash Tiering BIG DATA Flash Tiering SFA OS Designed for Enterprise Applications only e.g. Databases with OLTP workloads Designed for Big Data e.g. Life Sciences, Rich Media, FSI, O&G Cannot differentiate between IOPS and Streaming Workloads Analyzes and adapts to IOPS and Streaming Workloads differently Limited - Works in 1 mode, typically as an extended read cache Comprehensive - works in 4 modes to handle any workload and application Poor Cache Utilization - Promotes/demotes data to Flash based on best guesses Higher Cache Utilization Application/FS hinting promotes/demotes data SFX The ONLY Flash Caching Technology designed for Big Data 5
SFX Precision Engineered for Big Data DirectMon Enterprise Hadoop Appliance Petascale Lustre Storage Enterprise Scale-Out File Storage Completely Integrated SFX Management hscaler SFX integrates with the job scheduler & HDFS to accelerate time to results EXAScaler Accelerate data and metadata using SFX pools to speed up file system with minimal SSDs GRIDScaler Build a hyper optimized NAS and Parallel File System using SFX pools to accelerate metadata and lower TCO Built into the Storage Fusion Architecture Core Storage Platforms SFA12K 40GB/s/1.7M IOPS 1,680 Drives: 16U-2 Racks Embedded Computing SFA7700 12GB/s, 350K IOPS 60 Drives in 4U; 396 Drives in 20U
The Importance of Cache For Production I/O A Day In the Life of A Multi-Disciplinary Cluster Write Distribution for Multi-Disciplinary HPC Cluster Moore s Law Is Creating An I/O Blender Effect 90% of I/Os in a standard HPC environment are <32KB o Ref: Previous ORNL Slide Cache is critical in aligning all-too-frequent unaligned writes and capturing small writes to preserve performance DDN offers cache mirroring & battery-backed RAM and Flash cache to accelerate all data
DDN ReACT: Real-Time Adaptive Cache Technology Traditional Storage Caches Are Congested By Competing Streaming and Random Workloads, Resulting in Poor Performance In Mixed Workloads Full Stripe Cache Partial Stripe Battery-Backed SFA L2 Cache High-Speed Cache Link Battery-Backed SFA L2 Cache With DDN, Avoid write mirroring penalty, more headroom for random, unaligned I/Os
Partial Writes to RAM Aligned Writes To Flash The Many Dimensions of SFA Acceleration ReACT Context Commit API In-Band - Out Of Band Usage Based and Priority-Based Caching DRAM Cache SFX Write End of 2013 Instant Commit SFX Read HDD Tier Cache Flush Cache Warm
Bandwidth Ahmdal s Law & Quality of Service The Problem At Scale Disk failures and rebuilds are common at scale Traditional systems are not designed to protect application performance from HDD recovery Striped files and databases are especially vulnerable to any one drive failure 1 slow drive can slow down 1000 The SFA Solution Overprovisioned processing and internal network to handle data recreation Quality of Service timeouts for read operations where users enjoy deterministic response times SFX to buffer reads and writes* * 2014 Sustained Performance In A 100+ HDD Environment DDN Sustained Performance Competing Sustained Performance Time
SFX Application-Aware Caching How it works Applications Current Time Application Workload Application HINTS To Pre-fetch Block A B Needs Block A Needs Block B Controller 1 Controller 2 NVRAM SFX Tier NVRAM Data is moved from Rotating to Faster SFX Tier SSDs Rotating Media
SFX Use Case Budget Optimized I/O Without SFX With SFX Goal Max IOPS for $720K Goal Max IOPS for $720K 1000 SAS Drives 90 SSDs + 200 SATA Drives............ +...... IOPS: 200K Capacity: 600TBs Power: 15,000 W Response Times: 3.4ms IOPS: 3.5M Capacity: 600TBs Power: 2558 W Response Times: 0.2ms 16 Times Higher IOPS for same $ 80% lower power consumption 95% better response time
Summary Flash Is Ready For Prime Time System Architectures Should Be Smart I/O Subsystems Ram vs. Flash Cache Automated Acceleration Applications Can Now Be Even Smarter Application-Defined Acceleration To Optimize TCO
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