Power Consumption in Memory-Intensive Databases

undefined
 
Scaling the Memory Power Wall
with DRAM-Aware Data Management
 
Raja Appuswamy
 
Matthaios Olma
 
Anastasia Ailamaki
 
Ecole Polytechnique Fédérale de Lausanne (EPFL)
Power-Hungry MMDBs?
2
Energy efficiency - The New Holy Grail
What is the energy behavior of MMDBs?
Energy in MMDB
3
*HP Power advisor, six-core Intel Xeon E7-4809v2
4-socket - 24 cores, 96 DIMM slots
 
2.5x loaded
CPU Loaded (Watt)
 
1.25x idle
 
16x capacity
3x power
 
4x capacity
10x power
DRAM will emerge as a major contributor
Server Memory Trends
4
Processor: Intel Xeon E5 2 sockets, 4 channels/socket
Capacity – Performance Tradeoff
Capacity – Power Tradeoff
Capacity Limitation
Big data => Big memory => Big power bills
MMDBs should focus on reducing DRAM power
 
Reducing DRAM Power Draw
 
Hardware features
Frequency scaling
Power-down modes
Limitations
Enabled/disabled statically at boot time
Power-down state transition controlled by MC
 
5
What is the impact on MMDB performance?
What is the effectiveness under MMDB workloads?
 
Experimental Setup
 
Hardware
2 GHz 
Intel Xeon E5-2640 v2 CPU (2 socket, 8 cores/socket)
256 GB of memory (16x 16 GB RDIMMs)
Micro-benchmarks
Concurrent Scans 128 MB of int64s
Parallel Aggregation (a = ∑(b(i)+c(i))) over 8-GB 
double
 arrays
1-8 threads affinitized to a single socket to avoid NUMA
Macro-benchmarks (in paper)
TPC-C, TPC-H
 
6
Power Breakdown (Scans)
7
DRAM contribution is high even at 100% CPU
Even with 256 GB RDIMMs, DRAM contributes 30%
Impact of Frequency Scaling
8
Most energy-efficient is not always the fastest
8 threads
Memory Bandwidth Utilization
9
Bandwidth-intensive workloads suffer
(aggregation)
(aggregation)
Impact of Power-Down Modes
10
No impact on
performance or power
13% reduction in
power
Exploiting DRAM Idleness
11
MC good at predicting idleness
Memory Controller is conservative
>40% residency, yet
negligible power
savings
 
Can We Do Better?
 
DRAM-aware memory layout
Separate hot/cold data to enable longer idle time
DB-driven gear shifting
Pairing data and power modes
Multitier memory and storage tiering.
Rethinking anti-caching, 5-minute rule for two-level DRAM
Tiering-aware query optimization
Making optimizer aware of access latencies
 
 
12
 
Conclusion
 
Perfect storm
MMDBs need more DRAM to meet application demands
DRAM technology poses power-performance tradeoffs
Promise
Frequency scaling/low-power modes can limit power draw
Hurdles
Need software-driven DRAM DVFS and state transitions
Need to redesigns MMDBs to exploit hardware features
 
“It’s the memory, stupid!”
   
        - Richard L. Sites
 
13
Slide Note
Embed
Share

This collection of research delves into the power challenges faced by memory-intensive databases (MMDBs) and explores strategies for reducing DRAM power draw. Topics covered include the impact of hardware features on power consumption, experimental setups for analyzing power breakdown, and the effectiveness of reducing DRAM power draw under MMDB workloads. The findings emphasize the significance of managing DRAM power to mitigate energy costs and improve MMDB performance.

  • Power consumption
  • Memory-intensive databases
  • DRAM power
  • Hardware features
  • Experimental setups

Uploaded on Sep 27, 2024 | 0 Views


Download Presentation

Please find below an Image/Link to download the presentation.

The content on the website is provided AS IS for your information and personal use only. It may not be sold, licensed, or shared on other websites without obtaining consent from the author. Download presentation by click this link. If you encounter any issues during the download, it is possible that the publisher has removed the file from their server.

E N D

Presentation Transcript


  1. Scaling the Memory Power Wall with DRAM-Aware Data Management Raja Appuswamy Matthaios Olma Anastasia Ailamaki Ecole Polytechnique F d rale de Lausanne (EPFL)

  2. Power-Hungry MMDBs? Year End-use Elec. Bills Power plants (500MW) CO2 (US) Million MT Energy B kwh ($B) $9.0 2013 97 91 139 47 37 51 17 Energy efficiency - The New Holy Grail 2020 $13.7 147 50 2013-2020 incr. What is the energy behavior of MMDBs? $4.7 2

  3. Energy in MMDB *HP Power advisor, six-core Intel Xeon E7-4809v2 4-socket - 24 cores, 96 DIMM slots 1 DRAM Idle (Watt) Power (kWatt) 2.5x loaded DRAM Loaded (Watt) 4P Loaded (W) CPU Loaded (Watt) 0.5 1.25x idle 16x capacity 3x power 4x capacity 10x power 0 DRAM total capacity DRAM will emerge as a major contributor 3

  4. Server Memory Trends Processor: Intel Xeon E5 2 sockets, 4 channels/socket Memory Type Max Capacity Latency (nsec) Bandwidth (GB/s) Loaded W/GB Idle W/GB 153.7 Capacity Limitation UDIMM 128 72 0.2 0.02 768 Capacity Performance Tradeoff LRDIMM 235 40.4 0.15 0.07 Capacity Power Tradeoff HCDIMM 768 161.9 63.9 0.74 0.37 Big data => Big memory => Big power bills MMDBs should focus on reducing DRAM power 4

  5. Reducing DRAM Power Draw Hardware features Frequency scaling Power-down modes Limitations Enabled/disabled statically at boot time Power-down state transition controlled by MC What is the effectiveness under MMDB workloads? What is the impact on MMDB performance? 5

  6. Experimental Setup Hardware 2 GHz Intel Xeon E5-2640 v2 CPU (2 socket, 8 cores/socket) 256 GB of memory (16x 16 GB RDIMMs) Micro-benchmarks Concurrent Scans 128 MB of int64s Parallel Aggregation (a = (b(i)+c(i))) over 8-GB double arrays 1-8 threads affinitized to a single socket to avoid NUMA Macro-benchmarks (in paper) TPC-C, TPC-H 6

  7. Power Breakdown (Scans) CPU DRAM 100% Power Consumption (%) 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% DRAM contribution is high even at 100% CPU 1 2 3 4 5 6 7 8 # threads Even with 256 GB RDIMMs, DRAM contributes 30% 7

  8. Impact of Frequency Scaling 8 threads 1.6 Scans Aggregation (Normalized w.r.t 1600 MT/s) 1.4 Performance/Watt 1.2 1 0.8 0.6 0.4 0.2 0 800 1067 1333 1600 Memory Frequency (MHz) Most energy-efficient is not always the fastest 8

  9. Memory Bandwidth Utilization 45000 800 MHz 1067 MHz (aggregation) 1333 MHz 1600 MHz Memory Bandwidth (MB/s) 40000 35000 30000 25000 20000 15000 10000 5000 0 1 2 3 4 5 6 7 8 Bandwidth-intensive workloads suffer # threads 9

  10. Impact of Power-Down Modes (aggregation) No impact on performance or power 13% reduction in Power power Throughput 1.2 (Normalized w.r.t. disabled modes) Power and Performance 1 0.8 0.6 0.4 0.2 0 1 8 # threads 10

  11. Exploiting DRAM Idleness Power-down (Scan) Power-down (Agg) Active (Scan) Active (Agg) negligible power savings >40% residency, yet 100 Power-Down Residency (%) 80 60 40 20 0 1 8 MC good at predicting idleness Memory Controller is conservative # threads 11

  12. Can We Do Better? DRAM-aware memory layout Separate hot/cold data to enable longer idle time DB-driven gear shifting Pairing data and power modes Multitier memory and storage tiering. Rethinking anti-caching, 5-minute rule for two-level DRAM Tiering-aware query optimization Making optimizer aware of access latencies 12

  13. Conclusion Perfect storm MMDBs need more DRAM to meet application demands DRAM technology poses power-performance tradeoffs Promise Frequency scaling/low-power modes can limit power draw Hurdles Need software-driven DRAM DVFS and state transitions Need to redesigns MMDBs to exploit hardware features It s the memory, stupid! - Richard L. Sites 13

More Related Content

giItT1WQy@!-/#giItT1WQy@!-/#giItT1WQy@!-/#giItT1WQy@!-/#