TidalScale has created a software defined computer. At TidalScale, we have created a simple cost-effective way for a data scientist, an analyst, an engineer, a scientist, a database administrator, or a software developer to access a group of servers through a single operating system instance as if it were a single supercomputer. This dramatically simplifies development, while reducing software scaling complexity not to mention a dramatic cost saving in hardware and software. We configure hosted hardware into one or more TidalPods. Each TidalPod is a virtual supercomputer comprising a set of commodity servers configured with the TidalScale HyperKernel. What the user sees is standard Linux, FreeBSD or Windows running with the sum of all memory, processors, networks, and I/O. The secret sauce is the HyperKernel that fools the guest OS into thinking it’s running directly on a huge, expensive machine when in fact it’s running on a set of smaller, less expensive servers. We offer an incredibly simple user experience. • Define the computer size you want (Number of CPU, Amount of Memory), boot the virtual machine, then login to the computer… Thus, we enable a simple cost-effective way for a data scientist, an analyst, an engineer, a scientist, a database administrator, or a software developer to access a group of servers in a Datacenter through a single operating system instance as if it were a single supercomputer. This dramatically simplifies development, while reducing software scaling complexity not to mention a dramatic cost saving in hardware and software.

1. Scale | Simplify | Optimize | Evolve
RESTORING DEVELOPER PRODUCTIVITY THROUGH SIMPLICTY
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2. Industry Context
Data sizes for data under management are monotonically increasing
◦ Who wants less data?
Our appetite for analysis is monotonically increasing
◦ Do you think, or do you know?
◦ Trend toward evidence-based management
Our appetite for speed is monotonically increasing
◦ Who wants questions answered more slowly?
◦ Hence the industry interest in in-memory data management systems
Our overall ability to manage complexity is not increasing
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3. Virtualization today:
Is about Single Node Resource Sharing
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Solution scaling constrained by physical machine cost and size
Result: Finite resource pool

4. Scale-Out today:
An Architecture of Developer Complexity
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Solution scaling is constrained by the programmers ability to comprehend and
navigate the architectural complexity
Result: Increased development cost

5. TidalScale enables:
An Architecture of Simplicity – One Operating System
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Solution scaling enabled by aggregating hardware resources via one OS instance
Result: Lower development complexity, lower development cost

6. Why?
Creates a unique scalable solution experience:
User experience bare metal User experience TidalScale
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Real Screen Shots
No API’s to learn – define the machine size - boot it up

7. Fault Tolerance
System Standby and Linux Tool Transparency
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Active Server Standby Server
Transparent access to Linux Availability Tool Chain
TidalScale handles the underlying complexity

8. Result
AnyLogic™ 1 Million Agent Simulation*
Normal run time - 3 Days
TidalScale run time - 30 minutes
We changed the customers approach to using AnyLogic™
and rapid prototyping of simulations
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*No code changes were required

9. What Kinds of Problems Benefit?
Data Mining / Finance
◦ High Data Volumes, Large Analytics, Risk Analysis, Fraud Detection, Graph Analytics using
Alternative Data Sources, Risk modeling, High Frequency trading, Complex Event Modeling
Bioinformatics
• Next Generation DNA sequencing, Meta-genomic analysis, Finite Element Brain Modeling,
Time-Series MRI Neuro-Imaging
IT / Operational Systems
◦ In-House Applications, Web Controllers & Servers, Gateways, Image serving, Ad serving, OLTP,
ERP, Business Intelligence
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10. Data Mining: High Data Volumes
Scenario
◦ Analytic app running on popular SQL database
◦ Well established – trained users, tied to diverse apps & tools
◦ Running well on a single system, except…
Problem
◦ …Data volumes steadily growing…
◦ …Frequently upgrading hardware…
◦ …About to hit a wall
The TidalScale Solution
◦ Scale up existing application & move more data into memory cache for real time performance
◦ Avoid re-architecting DB & rewriting applications for scale out/cluster
◦ Avoid expensive maintenance cost of a cluster as shape of data changes or use cases & query types evolve
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11. Data Mining: Large Scale Analytics
Scenario
◦ Analytic app running on popular SQL database
◦ Well established – trained users, tied to diverse apps & tools
◦ Proven, reliable application needing no changes, other than the need to increase capacity
Problem
◦ Number of users and connections is growing
◦ Each user is generating large, demanding queries
◦ Frequently upgrading hardware
◦ About to hit a wall – add a second, third copy and offload users?
◦ But then you have a copy/synch problem, especially if data ever gets updated
The TidalScale Solution
◦ Assemble a bigger computer to move more data into memory cache & take advantage of more CPUs
◦ Avoid re-architecting DB for scale out/cluster
◦ Avoid administering multiple servers & users, and copying/synching data
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12. Data Mining: Mortgage Risk Analysis
Scenario
◦ New risk assessment tools for loans, mortgages
◦ Use of “alternative data” in addition to credit bureaus
Problem
◦ Mortgage application workload gets “peaky” as consumers react & play the interest rate game
◦ Backlogs develop during peaks, loan processing still remarkably hands-on process. When backlogs build, customers
leave
TidalScale Solution
◦ Enables a big, flexible computer that scales to handle more complex processing
◦ Supports processor intensive analytics of unstructured data, example NLP (natural language processing)
◦ Easy system expansion as data volumes grow from the addition of unstructured data
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13. Data Mining: Fraud Detection
Scenario
◦ New types of tools to detect fraud
◦ Ex: Graph DBs to see coordinated activity from disparate data
Problem
◦ Graph databases inherently require a closely coherent (single system) view
◦ As system grows, you must either
◦ Split the graph – at the risk of losing potential connections – leaves places for bad guys to hide
◦ Reduce granularity of data – abstracting your view – loss of detail
TidalScale Solution
◦ Enable full detailed view of transactional and account data across all customers over time
◦ Run the graph in-memory for real-time performance
◦ Run system on entirely standard hardware, OS & operational infrastructure
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14. What enables the solution?
The Memory Hierarchy in Human Terms
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(.3ns = 1s)
Event Latency Scaled
1 CPU Cycle 0.3 ns 1 s
Level 1 Cache Access 0.9 ns 3 s
Level 2 Cache Access 2.8 ns 9 s
Level 3 Cache Access 12.9 ns 43 s
Main Memory Access (DRAM, from CPU) 50.0 ns 3 min
Memory over Ethernet 3.2 μs 3.2 hours
CPU Context State Transfer 6.0 μs 6.0 hours
Flash SSD (PCI-e) 4.7 ms 5 months
Rotational disk I/O 1-10 ms 1-12 months
Internet: San Francisco to New York 40 ms 4 years
Internet: San Francisco to United Kingdom 81 ms 8 years
Internet: San Francisco to Australia 183 ms 19 years
TCP packet retransmit 1-3 s 105-317 years

15. TidalScale
Completely Transparent - No Changes Required
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…and many other applications

16. Hardware Example
System features
• Admin node 1
• Worker nodes 25
• Total Memory 3.2TB
• Total Cores: 150
• Network 1/10GbE
• Storage FreeNAS, xTB
Components
• 1x Admin node (A003)
• Colfax CX1260i-X6 Haswell, E5-2603V3 6C, 16GB
• 25x Worker nodes
• Colfax CX1260i-X6 Haswell, E5-2603V3 6C, 128GB
• 1x 1G switch
• 2x 10G switch (S009, S010) Mellanox
• 1x NAS
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17. TidalScale Benefits Summary
In-Memory Performance
◦ The world wants data to be in-memory, but hasn’t been able to get it.
◦ Historically the industry has scaled applications to fit on available computer hardware. Now, for the first time, the
industry can scale the hardware to fit the application.
Linear System Scalability
◦ TidalScale makes it possible to organically grow hardware using low cost commodity servers at linear cost, as
customer needs evolve.
◦ Applications can now achieve superior in-memory performance using inexpensive unmodified hardware.
Reduced Software Development Costs
◦ TidalScale uses off-the-shelf, unmodified Linux.
◦ TidalScale requires no changes to applications or database software.
◦ Optimization happens automatically using our software, and learns over time.
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18. Price/Performance at Scale
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Scale up costs increase
rapidly after 3Tb…

19. Performance Scales Up
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TidalScale can continue to
add cores as well as memory
(and bandwidth)
Single machine solution can
only add memory

20. Scale, Simplify, Optimize, Evolve
Scale:
◦ Aggregates compute resources for large scale in-memory analysis
and decision support
◦ Scales like a cluster using commodity hardware at linear cost
◦ Allow customers to grow gradually as their needs develop
Simplify:
◦ Dramatically simplifies application development
◦ No need to distribute work across servers
◦ Existing applications run as a single instance, without
modification, as if on a highly flexible mainframe
Optimize:
◦ Automatic dynamic hierarchical resource optimization
Evolve:
◦ Applicable to modern and emerging microprocessors, memories,
interconnects, persistent storage & networks
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21. Scale | Simplify | Optimize | Evolve
RESTORING DEVELOPER PRODUCTIVITY THROUGH SIMPLICTY
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