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Supercharging Data Performance for Real-Time Data Analysis

The velocity and volume of data are growing faster than ever before, and companies are looking for new methods to speed their data analytics. Using an innovative FPGA-based architecture, the Ryft ONE supercharges data analytics and provides you more value from your data.

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Supercharging Data Performance for Real-Time Data Analysis

  1. 1. 11 Supercharging Data Performance for Real-Time Data Analysis
  2. 2. 2 Information—the fuel of business—is trapped in analysis platforms built on 70-year old architectures.
  3. 3. 3 Data volume and velocity challenge traditional computing methods Traditional Approach: • Commodity x86 based servers • Cluster with open source software • Scale for volume • Scale for parallelism / performance Challenges: • High level languages can be inefficient • Data intensive workloads drive in-memory solutions • DRAM footprints at commodity prices are small • Scaling out increases cost and complexity
  4. 4. Ryft delivers huge benefits in a small package. Highest performance per watt and lowest total cost of ownership (TCO) of any product on the market. 48 TB in 1U • Data storage is abstracted as a set of Linux mount points • Support native encryption/decryption with no loss in performance (AES 256 Encryption) Simple API • C library abstracts internal FPGA constructs to simplify programmability, allowing a programmer to invoke operations as simple function calls, returning simple results • Command line • Web Interface Linux Front End • Linux (Ubuntu 14.04 LTS ) front end - Standard build, Non restricted OS, apt-get • API calls FPGA fabric backend • Linux services/protocols can be used • ssh/scp/rsync/sftp • Standard monitoring agents • Web services • Security configuration
  5. 5. x86 Architecture vs. Systolic Arrays Memory PE One Clock Cycle (x86) Memory PEPEPE PE PEPE One Clock Cycle FPGA- Systolic Array 100 ns 100 ns
  6. 6. FPGA Benefits x86 FPGA • General purpose computing • Sequential in nature • Non-deterministic performance • Interrupts • Memory allocation • Problems are broken into a sequence of operations and processed serially • Increasing number of instructions • Increased overhead • Increasing required power/cooling required • Software can break problems down and bring parallelism: • Between processors/cores • Between servers • Output combined over interconnects • Not general purpose • Purpose built algorithms • Can be reprogramed via firmware • Parallel in nature • Can execute many parallel operations in one clock cycle • More output with less power and clock speed • ~1000X less instructions to solve the same problem as x86 • 100% deterministic performance • No memory fetching or management • No interrupts
  7. 7. Multi-Dimensional Systolic Arrays PE PE PE PE PE PE PE PE PE PE PE PE PE PE PE PE PE PE PE PE PE PE PE PE PE
  8. 8. The Ryft ONE is powered by a breakthrough in Real-time Data Analysis. The only 1U platform capable of analyzing streaming, historical, unstructured, and multi-structured data in real-time at 10 GB/second. Ryft ONE avoids bottlenecks that strangle conventional systems by combining these two innovations: The Ryft Analytics Cortex™ Ryft ONE leverages a massively parallel bitwise computing architecture to deliver unprecedented performance from the smallest possible form factor. The Ryft Algorithm Primitives™ Library Each Ryft ONE comes with a subscription to this growing collection of pre-built algorithm components, and an open API to leverage them. +
  9. 9. “We see Spark Streaming scales nearly linearly to 100 nodes, and can process up to 6 GB/s at sub-second latency on 100 nodes for Grep, 2.3 GB/s for the other, more CPU-intensive jobs” UC Berkley Streaming Computation at Scale Proprietary | 9
  10. 10. Ryft transforms datacenter economics. The Ryft ONE Costly & Complex Clusters Search = 10 GB/s Term Frequency = 2.5 GB/s Search = 6GB/s Term Frequency= 2.3 GB/s
  11. 11. Wikipedia Examples • English XML Dump is offered by Wikipedia • Total Corpus is 44GB • Copying the data takes 44 seconds • Fuzzy search would take 4.4 seconds • Term Frequency would take 17.6 seconds
  12. 12. Data Exploration Use Case
  13. 13. Data Exploration Use Case • RDF—understanding of native formats • Powerful no-index search • Flexible query format with wildcarding • Identify relationships between disparate data
  14. 14. HDFS Data Triage for Hadoop/Spark Use Case Raw Data M/R noSQL Hive Text Index Application Hours? Days?
  15. 15. Search / Minimize @10GB/s Data Triage for Hadoop/Spark Use Case Ingest @ 1-4GB/s Seconds! HDFS • Social media signal/noise • Fuzzy searching at line rate @badguy1 @badguy2 @badguy01 @badboy01 Search: “badguy??”
  16. 16. Organizations who want real-time insights into all their data Large data sets (changing, structured & unstructured, Text, Binary, Imaging) High Velocity Data • Logging • Ad Data • Twitter Forensics & Legal Discovery • Host based forensics • E-discovery Scientific Data • Genomics • Sensor Data Financial • Compliance • Fraud Detection Cyber Security • PCAP • Full packet capture • Binary Analysis Imagery Analysis • Change Analysis • High Performance Rendering
  17. 17. Revisiting Performance Results Ryft ONE closes the industry’s data analytics performance gap by combining the following into a single architecture:  Parallel FPGA architectures to accelerate performance  Dedicated storage/access/RAM  Elimination of data security performance bottlenecks  Elimination of operating system and high level language overhead  Minimizing the need to move data Use Case Single Ryft ONE Throughput Spark Cluster to Match Performance Search ~10GB/sec > 100 nodes1 Fuzzy Search ~10GB/sec 100-200 nodes2 Term Frequency ~2.5GB/sec 100 nodes1
  18. 18. Accelerate business insights with the only platform purpose-built to simultaneously analyze any type of data—historical and streaming, unstructured and multi-structured— 100X faster with 70% lower TCO. The Ryft ONE: More data. Less center. Faster insights.
  19. 19. 1919 Questions