Embedded System Lab.

Embedded System Lab.최 길 모

Kilmo Choirlfah926@naver.com

Active Flash: Towards Energy-Efficient, In-Situ Data Analytics on Extreme-Scale Machines

Devesh Tiwari, Sudharshan S. Vazhkudai, Youngjae Kim, Xiaosong Ma, Simona Boboila, and Peter J. Desnoyers

Embedded System Lab.최 길 모

Contents

Background

Problems and Challenges

Active Flash Approach for In-situ

Active Computation Feasibility

Evaluation

ActiveFlash Prototype based on OpenSSD Platform

Conclusion

Embedded System Lab.최 길 모

Background

Embedded System Lab.최 길 모

Background Scientific Discovery : Two-Step

Scientific Simulation

Scientific Discovery

Data Analysis and Visualization

Embedded System Lab.최 길 모

Background Large-scale leadership computing applications produce big data

GTC produces ~30TB output data per hour at-scale.

Embedded System Lab.최 길 모

Problems and Challenges Offline approach suffers from both performance and energy inefficien-

cies

Redundant I/O(simulations write, analyses read)

Excessive data movement

Extra energy cost

Energy efficiency will become the primary metric for system design,

as compute power is expected to increase by x1000 in the next

decade with only a x10 increase in power envelope

Using simulation nodes for data analysis not acceptable

Embedded System Lab.최 길 모

Active Flash Approach for In-situ SSDs now being adopted in Supercomputers(e.g. Tsbame, Gordon)

higher I/O throughput and storage capability

SSD controllers becoming increasingly powerful

multi-core low-power processors

Idle cycles at SSD controllers

In-situ analysis

analysis on in-transit output data, before it is written to the PFS

eliminates redundant I/O, but it use expensive compute nodes

Embedded System Lab.최 길 모

Active Flash Approach for In-situ Active flash

In-situ analysis on SSDs Exploit the computation at idle cycles of the SSD controller Reduce transfer costs high performance and energy saving

Embedded System Lab.최 길 모

Active Flash Approach for In-situ Three approach to data analysis

offline active flash analysis node

Embedded System Lab.최 길 모

Active Computation Feasibility

Modeling SSD Deployment

Multiple constraints

Capacity

Enough SSDs to sustain output burst

Performance

High I/O bandwidth to SSD space

Fast restart from application checkpoints

Write durability

SSD write endurance limits

Embedded System Lab.최 길 모

Active Computation Feasibility Staging Ratio

How many simulation nodes share one common SSD?

Embedded System Lab.최 길 모

Active Computation Feasibility Modeling active computation feasibility

Relatively less compute intensive kernels better suited for active computa-

tion(e.g. regex matching)

Dependent on multiple factors : simulation data production rate, staging

ratio, I/O bandwidth, etc.

Embedded System Lab.최 길 모

Evaluation Cray XT5 Jaguar supercomputer

Samsung PM830 SSD

Intel Core i7 processors

Embedded System Lab.최 길 모

Evaluation Feasibility of the analysis node approach

Most data analysis kernels can be placed on SSD controllers without degrading

simulation performance

Additional SSDs are not required for supporting in-situ data analysis on SSDs

Analysis node approach is feasible at higher staging ratios, but at additional infra-

structure cost

Embedded System Lab.최 길 모

Evaluation

Energy and cost saving analysis Staging ratio = 10

Active Flash and offline approach : y1

analysis node : y2

Offline model consumes more energy

due to the I/O wait time

Embedded System Lab.최 길 모

Conclusion Extant approaches to scientific data analysis(e.g. offline and analysis

nodes) are stymied by several inefficiencies in data movement and

energy consumption that results in sub-optimal performance

Active flash is better than either approaches for all of the aforemen-

tioned metrics