Kilmo Choi rlfah926@naver
description
Transcript of Kilmo Choi rlfah926@naver
Embedded System Lab.
Embedded System Lab.최 길 모
Kilmo [email protected]
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
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Background Large-scale leadership computing applications produce big data
GTC produces ~30TB output data per hour at-scale.
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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
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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
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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
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Active Flash Approach for In-situ Three approach to data analysis
offline active flash analysis node
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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
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Active Computation Feasibility Staging Ratio
How many simulation nodes share one common SSD?
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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.
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Evaluation Cray XT5 Jaguar supercomputer
Samsung PM830 SSD
Intel Core i7 processors
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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
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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
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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