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May 2011

Leveraging the DRAM Process Technology for Power Saving in Server Systems

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Contents

1. Overview of Green Memory Power Consumption .............................. 6

2. Memory Considerations for Large IT Investments .............................. 7

2.1. Data Traffic Load Increase ....................................................................... 7

2.2. Cloud Computing ...................................................................................... 9

3. Data Center Costs ........................................................................... 10

4. Case Study ...................................................................................... 11

4.1. Test Environment .................................................................................... 11

4.2. Power Consumption 1 ............................................................................ 12

4.3. Power Consumption 2 ............................................................................ 12

4.4. SPEC Power Benchmark ....................................................................... 13

4.5. Intel Westmere-EP [Dell R710] and DPC Concept ................................. 14

5. Power Usage Effectiveness (PUE) .................................................. 15

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Figures

Figure 1 – Data Traffic .......................................................................................................... 7

Figure 2 – Data Traffic Impact ............................................................................................... 8

Figure 3 – Virtualized Server Memory ................................................................................... 9

Figure 4 – CO2 Emissions ................................................................................................... 10

Figure 5 – Total Memory Power .......................................................................................... 12

Figure 6 – Total System Power ........................................................................................... 12

Figure 7 – SPEC Power for Case Study 1 ........................................................................... 13

Figure 8 – Memory Configuration of the Tested Dell R710 Server ...................................... 14

Figure 9 – PUE ................................................................................................................... 15

Table

Table 1 – Memory Conditions As Used ............................................................................... 11

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Preface

The purpose of this document is to describe about the role of DRAM process technology in power savings of server systems.

The audience for this guide comprises Microsoft Technology Centre (MTC) consultants, board designers, and companies who are interested in leveraging the DRAM process technology. The content presentation is based on the assumption that you are knowledgeable in the DRAM process technology and server systems.

Use the following links to jump to a specific location of your interest in this document:

Chapter 1: Overview of Green Memory Power Consumption

Chapter 2: Memory Considerations for Large IT Investments

Chapter 3: Data Center Costs

Chapter 4: Case Study1

Chapter 5: Case Study 2

Chapter 6: Power Usage Effectiveness (PUE)

This guide uses the following notation conventions.

Italic identifies new words, emphasizes phrases, or identifies document names.

Terms and Abbreviations

CAGR Compound Annual Growth Rate

CPU Central Processing Unit

DIMM Dual In Line Memory Module

CRAC Computer Room Air Conditioner

DPC DIMM Per Channel

EB Exabytes = 1018

GB Gigabytes=109

MTC Microsoft Technology Centre

PB Petabytes = 1015

POR Plan of record

OS Operating System

SPEC Standard Performance Evaluation Corporation

TÜ V Technischer Ü berwachungs-Verein, Technical Inspection Association

VM Virtualized Memory

Symbol Standards The IEEE/ANSI set of symbol standards were used throughout this document.

Related Documentation www.samsung.com/greenmemory

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References

1. ABI Research, Surge in Video Will Drive Global Data Traffic to More Than 60,000

Petabytes in 2016, (online) available from http://www.abiresearch.com (Accessed 5/7/11)

2. Gartner, available from http://www.gartner.com/technology/home.jsp

3. Rawson A, Pfleuger J, and Cader T, Green Grid Data Center Power Efficiency Metrics,

(online) available from http://www.thegreengrid.org/library-and-tools

4. SPEC, SPECpower_ssj2008, (online) available from http://www.spec.org/power_ssj2008/

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1. Overview of Green Memory Power Consumption

In November 2009, Microsoft and Samsung decided to collaborate in order to enable optimized performance and power efficiency for server systems. Microsoft CEO Steve Ballmer and Samsung Electronics Vice Chairman Lee Yoon-woo agreed on a joint push for green IT marketing. Since then, both the companies have jointly driven forward these initiatives.

The representative outputs of such joint efforts include the Green Memory collaboration between Microsoft Technology Center (MTC) and Samsung Electronics (pursued since 2010). In one such effort, Samsung Electronics‟ Green Memory (30nm Class) power consumption was measured when it was deployed on a server operated by Microsoft Windows Server 2008 R2 operating system. This array of work was conducted by MTC, who provided the Data Center Solution.

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2. Memory Considerations for Large IT Investments

The key factor while deciding server performance is not CPU capability, but memory capacity. It is important to consider the right choice of memory in large IT investment for the following reasons:

Massive power savings

Significant higher efficiency

Greatly improved performance

Since the memory capacity per server is increasingly becoming large, it is important to focus on the memory power consumption. For data centers with large installations of servers, the efficiency of memory power consumption has become the most critical aspect.

2.1. Data Traffic Load Increase

Across the globe, the data traffic load has increased by over 40% CAGR per year, mainly due to web and Internet traffic. There is a growing pattern of video and TV streaming from sites such as “YouTube” and other social media sites and longer media (such as TV shows and films).

Figure 1 – Data Traffic

To handle this huge amount of traffic, there is an urgent need for an expanded installation of hardware. However, such hardware expansion does not simply mean the expansion of the system scale. It requires the expansion of memory or storage systems that can store and handle a huge amount of data in the region of 31 EB (Exabytes = 10

18) each month (this is the

perabytes per month requirement in 2011). Both fast memory and storage performance are the two key differentiators when it comes to handling data traffic load. Figure 2 illustrates data traffic increase from 2008 to 2013.

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1PB* = 13.3 years of HD Video or 4.7Billion eBooks

* PB: Peta Bytes (1015

)

Source: Cisco Visual Networking Index

Figure 2 – Data Traffic Impact

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2.2. Cloud Computing

Cloud computing is designed to minimize the use of individual terminals‟ resources. The concept behind cloud computing is to use the virtual server resources for individuals‟ work by connecting to the network.

Consumers are increasingly turning to web-based applications instead of purchasing their own software - be it typical office applications or media storage. The advantage for the user here is that there is no need to back up data at home; access to some of the software is free or a very small cost.

Since users have connected mobile devices and access to the Internet, both network and network equipments will have to be scaled. Therefore, server capacity needs to be increased to meet this demand.

As more and more users turn to Cloud Computing, larger virtualized server memory is required to ensure a faster response. Server Virtualization is a new solution designed to integrate the numerous physical servers into a high-performance virtual machine on the server. It aids in maximizing the utilization of resources, thereby boosting IT flexibility.

If the ratio of memory per server system increases, it will lead to an increase in the power consumption. Figure 3 – Virtualized Server Memory shows an example of how this can increase.

Figure 3 – Virtualized Server Memory

It can be seen that when a 48GB solution is used, 26% of the memory power is used for on-board memory, compared to only 15% when a 16GB solution is used.

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3. Data Center Costs

Most hi-tech companies use large facilities to house their computer systems, telecommunications, and storage, in Data Centers. These facilities will generally have backup or redundant power supplies and controls to keep the environment stable (such as air conditioning) and systems for protection (such as fire control).

Typically, these data centers will have racks of equipment in one room, interconnected via cable trays, and sometimes, elevated floors. These facilities can be as small as a room in a building or in the case of large companies, can be a building all to itself. There are now standards for the design, construction, and operation of data centers; the costs of which can be quite significant.

To calculate the efficiency of data centers, some metrics have been devised. One such metrics is the Power Usage Effectiveness (PUE). For more information, see Section 5.

In 2015, it is predicted that the server operation costs will become larger than server purchase costs, and the energy costs associated will increase from under 10% of IT budget to over 50% (courtesy: Gartner).

According to the US Energy Information Administration, it is estimated that in the period ranging from 2007 – 2020, carbon dioxide emissions from data centers will annually increase by 11%. Also, world energy related carbon dioxide emissions will annually increase by 1% during this period. As illustrated in Figure 4 – CO2 Emissions, carbon dioxide emissions from the data centers will have a huge impact in the future.

Figure 4 – CO2 Emissions

Carbon dioxide emissions from data centers alone will represent 1.3% of the total emissions worldwide in 2020, outpacing those from airlines. This situation can be bettered from lower power solutions for data centers.

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4. Case Study

In the following case study, you will learn that Samsung Green Memory could help reduce the carbon dioxide emissions from data centers that populate their servers with 30nm memory products.

This case study will focus on the existing 50nm class server memory (Non-Green Memory) compared to the new 30nm class server memory (Green Memory) technology.

A joint effort was carried out with MTC and Samsung using Dell Servers at MTC Munich, Germany in March 2011.

4.1. Test Environment

Servers: 2 identical Dell R710 with Intel Westmere EP1, 2 sockets

OS: Microsoft Windows Server 2008 R2 Enterprise, Hyper-V, activated power saving, Virtual Machine

Memory: 7 configurations based on 8GB 2Rx4 for 30nm and 50nm classes server memory

Workload: Microsoft „Consume.exe‟ (freeware, but only 10% portion of active current)

Total Memory Green Memory (30nm Class)

Samsung Part number: M393B1K70DH0-YH9

Non-Green Memory (50nm Class) Samsung Part number: M393B1K70BH1-CH9

Memory Voltage 1.5V 1.35V 1.5V 1.35V

48GB (1DPC*) 1333Mbps 1333Mbps 1333Mbps

96GB(2DPC) 1333Mbps 1333Mbps

144GB(3DPC) 800Mbps 800Mbps

[Reference: Intel latest POR]

Table 1 – Memory Conditions As Used

Consume.exe is a utility from the Windows 2003 Resource Kit. The Resource Kit is a set of tools used by system administrators to manage servers. The Consume.exe program simulates the usage of the system resources such as physical memory, page file memory, disk space, CPU time, and kernel pool. For more information on Consume.exe, refer to the Microsoft Website at http://www.microsoft.com/download/en/details.aspx?id=17657.

1 (Westmere-EP is the codename for the 1-2 socket, up to six core server/workstation processor targeted for the Intel 5520 chipset based platform (compatible with Intel

Xeon 5500 platform). Westmere-EP is part of the family of 32nm processors codename Westmere based on Intel micro-architecture codename Nehalem and targeted for

production in 2010. Source: ark.intel.com)

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4.2. Power Consumption 1

In the test server systems, when the Microsoft „consume.exe‟ workload was given, the 30nm class was confirmed to consume 51% to 60% less total memory power compared with the 50nm class, according to memory combination conditions.

Figure 5 – Total Memory Power

4.3. Power Consumption 2

In the test server systems, when the Microsoft „consume.exe‟ workload was used, the 30nm class was confirmed to consume 5.5% to 11% less total system power compared with the 50nm class.

If 11% or 25W PUE (Power Usage Effectiveness) is assumed as 2, the data center will actually need 50W. This is equivalent to a saving of EUR 300 (KRW 466,000) based on a four-year period. If a data center has 1,000 servers, it can save around EUR 300,000 (KRW 466 million) for four years. (The four-year period is based on the usual memory replacement cycle with data centers.)

Figure 6 – Total System Power

* PUE refers to Power Usage Effectiveness. It is the indexation of the IT equipment power consumption to total data center power consumption ratio. For more information, see Section 5.

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4.4. SPEC Power Benchmark

In the test server systems, the representative server benchmark universal program „SPEC Power‟ workload was used. SPECpower_ssj2008 is the first industry standard benchmark that evaluates certain characteristics of servers. It will exercise the CPU, caches, memory hierarchy, among other things. There is a general methodology to complete these tests (available from the SPEC website).

Both 50nm class and 30nm class were compared in terms of system performance per watt with the 3 DIMM per Channel (DPC) condition (full loading using 2*CPU). The result is shown in the graph (see Figure 7 – SPEC Power for Case Study 1). Performance per watt refers to memory efficiency, which is a measure of how efficient the performance against power consumption is.

Figure 7 – SPEC Power for Case Study 1

The chart in Figure 7 suggests that Samsung‟s Green Memory has shown greater efficiency in Server Benchmark‟s representative program SPEC Power as well, and proves its reliability.

Load level refers to the quality of the workload. The green thick line refers to Green DDR3 and represents performance per watt. The red line refers to 50nm. As the value of the line increases, so does the efficiency. It can be seen that the Green DDR3 is far more efficient.

The thin solid line refers to consumption power (watt). The higher the line is, the greater the consumption.

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4.5. Intel Westmere-EP [Dell R710] and DPC Concept

Figure 8 – Memory Configuration of the Tested Dell R710 Server

This is the memory configuration of the tested Dell R710 server.

When only 1 DPC was tested, slot 0 is used. On 2 DPC then slot 0 and 1 is used.

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5. Power Usage Effectiveness (PUE)

A measure of a data center‟s energy efficiency is the PUE. It is the most important measure of how well a data center is designed. Established by the Green Grid consortium, the smaller it is, the more efficient it is. The current level average is 2.0. However, the target is to reach under 1.2.

To calculate data center Power Usage Effectiveness (PUE), use the following formula:

𝐏𝐔𝐄 = 𝐓𝐨𝐭𝐚𝐥 𝐝𝐚𝐭𝐚 𝐜𝐞𝐧𝐭𝐞𝐫 𝐩𝐨𝐰𝐞𝐫 𝐤𝐖

𝐓𝐨𝐭𝐚𝐥 𝐈𝐓 𝐩𝐨𝐰𝐞𝐫 (𝐤𝐖)

The data for this formula can be visualized in Figure 11.

Figure 9 – PUE

For example, if the total power used by the facility is 1000KW and the data center IT equipment power is 400KW, then the PUE is calculated as follows.

𝐏𝐔𝐄 = 1000

400= 2.5