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Holmes Hummel
JONATHAN KOOMEY: BLOG Articles summarizing the work appeared yesterday in The New York Times, Bloomberg , USA Today, Data Center Dynamics, Wired , Quartz, IFL Science, New Scientist, and One Zero, among other outlets. Google also did a blog post describing their progress in improving data center efficiency over time. The Northwestern University news releaseis here.
JONATHAN KOOMEY: BLOG Our latest research on comatose servers. I’ve been working with Jon Taylor of Anthesis Group and Aaron Rallo of TSO Logic to compile data on servers in enterprises that are using electricity but generating no useful computing output (we call these comatose servers).Until now, it has been difficult to compile data on idle servers over the network, but recent developments in measurement of JONATHAN KOOMEY: BLOG We show that a focus on what we call “typical use” efficiency reveals more rapid improvements than are evident in peak-output efficiency in the 2008 to 2016 period, as shown in Figure 2. This new article is an expanded look at the data we first put forth in IEEE Spectrum last year: Koomey, Jonathan, and JONATHAN KOOMEY: BLOG In a time of rapid technological change, it isn’t wise to rely on things staying static. The only thing constant is change! 1. Osborn, Julie, Frances Wood, Cooper Richey, Sandy Sanders, Walter Short, and Jonathan G. Koomey. 2001. A Sensitivity Analysis of the Treatment of JONATHAN KOOMEY: BLOG Let’s first look at an equation known as the Kaya Identity, which describes fossil carbon emissions as the product of four terms: Population, GDP/person (wealth), Primary Energy/GDP, and Carbon dioxide emissions/primary energy. Over time, analysts have realized that this four-factor identity collapses some important information. JONATHAN KOOMEY: BLOG More excuses from the Breakthrough Institute on data quality. The following is a joint post from Danny Cullenward and Jonathan G. Koomey. Image Credit: Michael Maggs JONATHAN KOOMEY: BLOGABOUTANALYTICSBLOGPRESSCLIENTSTALKS An Update On Trends In US Primary Energy, Electricity, And Inflation-Adjusted GDP Through 2019. Back in 2015, Professor Richard Hirsh (Virginia Tech) and I published the following article in The Electricity Journal, documenting trends in US primary energy, electricity, and real (inflation-adjusted) Gross Domestic Product (GDP) through 2014:. Hirsh, Richard F., and Jonathan G. Koomey. 2015. JONATHAN KOOMEY: ABOUT About Jon. I'm a researcher, author, lecturer, and entrepreneur whose work spans climate solutions, critical thinking skills, and the energy and environmental effects of information technology. I was a lecturer in Earth Systems at the School of Earth, Energy, & Environmental Sciences at Stanford University from November 2016 through June 2018. JONATHAN KOOMEY: RESEARCH Jonathan Koomey. White paper for Knovel, a division of Elsevier, 2014. Download a copy. Defining a Standard Metric for Electricity Savings. Jonathan Koomey, Hashem Akbari, Carl Blumstein et al. Environmental Research Letters, 2010. Download. Why 2 Degrees Really Matters. Jonathan Koomey and Florentin Krause. JONATHAN KOOMEY: ANALYTICS My company (Koomey Analytics, formerly Analytics Press) is the umbrella under which many of these activities reside. One important research area for me and my colleagues has been understanding the key drivers of global emissions scenarios, which culminated in this peer-reviewed journal article: Koomey, Jonathan, Zachary Schmidt,Holmes Hummel
JONATHAN KOOMEY: BLOG Articles summarizing the work appeared yesterday in The New York Times, Bloomberg , USA Today, Data Center Dynamics, Wired , Quartz, IFL Science, New Scientist, and One Zero, among other outlets. Google also did a blog post describing their progress in improving data center efficiency over time. The Northwestern University news releaseis here.
JONATHAN KOOMEY: BLOG Our latest research on comatose servers. I’ve been working with Jon Taylor of Anthesis Group and Aaron Rallo of TSO Logic to compile data on servers in enterprises that are using electricity but generating no useful computing output (we call these comatose servers).Until now, it has been difficult to compile data on idle servers over the network, but recent developments in measurement of JONATHAN KOOMEY: BLOG We show that a focus on what we call “typical use” efficiency reveals more rapid improvements than are evident in peak-output efficiency in the 2008 to 2016 period, as shown in Figure 2. This new article is an expanded look at the data we first put forth in IEEE Spectrum last year: Koomey, Jonathan, and JONATHAN KOOMEY: BLOG In a time of rapid technological change, it isn’t wise to rely on things staying static. The only thing constant is change! 1. Osborn, Julie, Frances Wood, Cooper Richey, Sandy Sanders, Walter Short, and Jonathan G. Koomey. 2001. A Sensitivity Analysis of the Treatment of JONATHAN KOOMEY: BLOG Let’s first look at an equation known as the Kaya Identity, which describes fossil carbon emissions as the product of four terms: Population, GDP/person (wealth), Primary Energy/GDP, and Carbon dioxide emissions/primary energy. Over time, analysts have realized that this four-factor identity collapses some important information. JONATHAN KOOMEY: BLOG More excuses from the Breakthrough Institute on data quality. The following is a joint post from Danny Cullenward and Jonathan G. Koomey. Image Credit: Michael Maggs JONATHAN KOOMEY: ABOUT About Jon. I'm a researcher, author, lecturer, and entrepreneur whose work spans climate solutions, critical thinking skills, and the energy and environmental effects of information technology. I was a lecturer in Earth Systems at the School of Earth, Energy, & Environmental Sciences at Stanford University from November 2016 through June 2018. JONATHAN KOOMEY: BLOG An Update On Trends In US Primary Energy, Electricity, And Inflation-Adjusted GDP Through 2019. Back in 2015, Professor Richard Hirsh (Virginia Tech) and I published the following article in The Electricity Journal, documenting trends in US primary energy, electricity, and real (inflation-adjusted) Gross Domestic Product (GDP) through 2014:. Hirsh, Richard F., and Jonathan G. Koomey. 2015. JONATHAN KOOMEY: BLOG A look at historical global trends in energy and emissions. As part of our work decomposing growth in greenhouse gas emissions into its key factors that was published in 2019 , we delved into historical data to create benchmarks against which trends in scenario projections could be compared. For our historical trends we relied on the long term data in the Primary, Final, and Useful (PFU JONATHAN KOOMEY: BLOG Dashboards of key drivers for the recently released IEA Sustainable Development Scenario (SDS) A couple of weeks ago, the International Energy Agency (IEA) released emissions scenarios related to the World Energy Outlook (WEO). We’ve used our spreadsheets to disentangle the JONATHAN KOOMEY: BLOG This corollary places special obligations on media producers and consumers (Koomey 2014). KNOW YOUR HISTORY. Information technology changes at a much more rapid pace than many other technologies (Nordhaus 2007, Koomey et al. 2011, Koomey et al. 2013, Koomey and Naffziger 2016). Unfortunately, innumerate observers love to mindlessly extrapolate JONATHAN KOOMEY: BLOG Google’s new white paper on clean energy for their data centers. Google just released this white paper, which is the next logical evolution in clean energy for data centers (also see the related blog post).When companies claim their data centers use 100% clean electricity, they do that using an annual balancing act, which sometimes isn’t clear to people not familiar with how this works. JONATHAN KOOMEY: BLOG Intellectual honesty is one way to judge the actions of politicians (or anyone else, for that matter). An intellectually honest position is one that has a fair chance of actually solving the problem that a policy proposal claims to address, and that is both consistent with the evidence and internally coherent (i.e., not self-contradictory). JONATHAN KOOMEY: BLOG The importance of standardized utility rate data for energy innovation. In response to a recent twitter thread, I dug up an idea I proposed more than a decade ago about requiring utilities to release their rate structures in a standardized structured electronic format.I originally proposed this in my testimony to the Joint Economic Committee of the United States Congress on July 30, 2008 JONATHAN KOOMEY: BLOG An old (2012) story with lessons that are still important today. I had at some point bookmarked this 2012 article containing a story from BP about reducing greenhouse gas (GHG) emissions and saving money.I’m posting it here now because the lesson it teaches is still importantand relevant.
JONATHAN KOOMEY: BLOG I just released my new study on data center electricity use in 2010. I did the research as an exclusive for the New York Times, and John Markoff at the Times wrote an article on it that will appear* About
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Jonathan G. Koomey Ph.D.*
TueJun 1
2021
I GAVE A VIRTUAL KEYNOTE TODAY FOR THE ITHERM 2021 TECHNICAL CONFERENCE, FOCUSING ON MISCONCEPTIONS ABOUT ELECTRICITY USE AND EMISSIONS ASSOCIATED WITH COMPUTING My keynote talk today for the iTherm 2021 technical conference is an expansion of points made in a commentary article by me and Professor Eric Masanet, UCSB,
which is “in press” at _Joule_ right
now (more when that’s published). I presented nine different high-profile misconceptions about electricity use and emissions associated with computing, explored four pitfalls that lead to such misconceptions, and suggested four ways we can do better in thefuture.
Here is the conclusions slide: You can download a PDF of the slides (which include three pages ofreferences) HERE
.
*
TueNov 24
2020
I GAVE A VIRTUAL TALK TODAY FOR THE ORGANIZATION FOR SECURITY AND CO-OPERATION IN EUROPE AND THE WORLD ENERGY COUNCIL ON THE ROLE OF ICT IN THE ENERGY TRANSITION My talk was titled “Information and communications technology (ICT) and the energy/climate transition”, and I presented it today (November 24, 2020) at the 3rd Vienna Energy Strategy Dialogue, on the Implications of the Global Energy Transition”, Vienna, Austria.The key points:
> • Direct electricity used by ICT is modest and hasn’t grown much > if at all in recent years.>
> • Nobody can credibly project ICT electricity use more than a few > years ahead, and exaggerations of ICT electricity use abound in the> literature.
>
> • ICT is a powerful source of emissions reductions throughout the > economy, which is why I call ICT our “ace in the hole” when it > comes to facing the climate challenge. To download a PDF version of the talk, click here.
*
ThuOct 8
2020
AN UPDATE ON TRENDS IN US PRIMARY ENERGY, ELECTRICITY, AND INFLATION-ADJUSTED GDP THROUGH 2019 Back in 2015, Professor Richard Hirsh (Virginia Tech) and I published the following article in _The Electricity Journal_, documenting trends in US primary energy, electricity, and real (inflation-adjusted) Gross Domestic Product (GDP) through 2014: > Hirsh, Richard F., and Jonathan G. Koomey. 2015. “Electricity > Consumption and Economic Growth: A New Relationship with Significant > Consequences?” _The Electricity Journal_. vol. 28, no. 9. > November. pp. 72-84.>
Every year since, my colleage Zach Schmidt and I have updated the trend numbers for the US using the latest energy and electricity data from the US Energy Information Administration (EIA). This short blog post gives the three key graphs from that study updated to 2019, and makes a few observations. FIGURE 1 shows GDP, primary energy, and electricity consumption through 2019, expressed as an index with 1973 values equaling 1.0. From 2017 to 2018, GDP grew a little more slowly and primary energy and electricity grew a little more rapidly than in recent years, but primary energy and electricity consumption both dropped in 2019 relative to the year before. GDP continued to show modest growth consistent with recent historical rates (all bets are off for 2020, though, given the likely effects of COVID-19). The overall picture really hasn’t changed that much. Electricity consumption and primary energy consumption have been flat for about a decade and two decades (respectively). FIGURE 2 shows the ratio of primary energy and electricity consumption to GDP, normalized to 1973 = 1.0. The trends there are pretty clear as well. Primary energy use per unit of GDP has been declining since the early 1970s, while the ratio of electricity use to GDP has been declining since the mid-1990s. Before the 1970s, electricity intensity of economic activity was increasing, and from the early 1970s to the mid-1990s, it was roughly constant. FIGURE 3 (which was Figure 4 in the Hirsh and Koomey article) shows the annual change in electricity consumption going back to 1950. Growth in total US electricity consumption has just about stopped in the past decade, but there’s significant year-to-year variation. The decline in 2019 electricity use almost offset the growth from 2017 to 2018 (and this decline predates the effects of COVID-19 on economic activity). Flat or declining consumption poses big challenges to utilities, whose business models depend on continued growth to increase profits (unless they are in states like California, where the regulators have decoupled electricity use from profits). If the US embarks on a sustained effort to #electrifyeverything, then these trends can be reversed, but that will take time, and in the meantime, the long running efforts on efficiency standards and labeling continue to have substantial effects on electricity consumption in developed nations. Email me at jon@koomey.com if you’d like a copy of the 2015 article or the latest spreadsheet with graphs. If you want to use these graphs, you are free to do so as long as you don’t change the data and you credit the work asfollows:
> This graph is an updated version of one that appeared in Hirsh and > Koomey (2015), using data from the US Energy Information > Administration and the US Bureau of Economic Analysis.>
> Hirsh, Richard F., and Jonathan G. Koomey. 2015. “Electricity > Consumption and Economic Growth: A New Relationship with Significant > Consequences?“ _The Electricity Journal_. vol. 28, no. 9. > November. pp. 72-84.>
*
TueApr 7
2020
A FUN SCIENCE PROJECT: A SIMPLE CLOUD CHAMBER! _SAFETY WARNING: THIS PROJECT INVOLVES DRY ICE, WHICH CAN REALLY DAMAGE YOUR SKIN IF YOU MAKE DIRECT CONTACT WITH IT. IF YOU ATTEMPT THIS ACTIVITY, USE APPROPRIATE SAFETY PRECAUTIONS (LIKE OVEN MITTS AND TONGS TO MOVE THE DRY ICE)_ When I was a kid I always wanted to make a cloud chamber, which makes vapor trails of atomic particles visible to the naked eye. I first learned about it from reading a book by C. L. Stong titled “TheAmateur Scientist
”,
which was a compilation of Stong’s columns in Scientific American. It’s an amazing book, and if you love tinkering as much as I do, it’s a terrific source of inspiration. It was published in 1960 (yes, I’m old) and I still have my copy (yes, I’m a bit of apackrat).
You can still order a used copy on Amazonfor almost
$60, but for the DIY science geek it’s well worth it (even today). Some of the chapters include “A homemade atom smasher”, “The Millikan oil-drop experiment”, “A simple magnetic resonance spectrometer”, “Homemade electrostatic generators”, “A low-speed wind tunnel”, “An electronic seismograph”, “A transistorized drive for telescopes”, and lots of other fun projects in many fields of science. The chapter on cloud chambers is very thorough, explaining many different designs and even showing how you can use magnetic fields to detect curvature in the particle tracks and determine exactly which types of charged particles they might be. Back in those days I didn’t have access to dry ice so never did the experiment, but now it’s available in every supermarket. When one of our boys needed a science project, I suggested this one, and he jumpedat it.
Nowadays there are many resources available online, and one of the best is the one by _Science Friday_,
but I want to describe some things we learned from doing it using that book from 1960 in case you want to try this yourself. The basic idea is to take a glass jar with a metal screw top, stuff a sponge in the bottom of the jar, pour some 90+% rubbing alcohol on the sponge, screw on the lid, invert it, place it on some dry ice, shine a flashlight from the side, and see what happens. When it works, you first see what looks like a tiny drizzle of alcohol droplets, then every so often (a few times a minute for us) you see a trail of condensed droplets that appears and then falls at the same rate as the alcohol “rain”. Those are atomic particles making their way through the alcohol clouds (see the Science Friday link above for examples of how these look). It’s not as simple I made it sound in the previous paragraph. The inside of the jar lid needs to be black, for contrast. The light needs to be just so. Your container needs to be clear enough for visibility. It’s important to choose the right container. Our first attempt used a pickle jar (the one we happened to have) that didn’t have super clear glass (it was a bit wavy). Once we got a better jar it worked great, so check the visibility through the glass before choosing a jar. We also tried this with a short (about 3″) tall jar, and that didn’t work as well because the glass frosted over from cold too quickly. Get a taller one (more like 6-8″ high). Some websites advocate using permanent marker on the inside of the jar lid to make it black, but we found that the alcohol removed the marker so this didn’t work so well. Based on advice from the Stong book, we ended up buying some velvet (about half a yard) from the fabric store and cutting a piece that was about 1.5 feet square. We placed this over the open jar and then screwed the top on (velvet side was insidethe jar).
When you flip that over, it looks like this. The nice thing about this setup is that you can cover the block of dry ice (ours was about 10″ square and 1.5″ high) with the velvet and the metal top conducts heat away from the metal top and through the velvet. Stong recommended adding a little alcohol to the velvet also (in addition to charging the sponge with it) and that seemed to work for us. The cloth also covers the dry ice and prevents dry ice “steam” from interfering from viewing. It also prevents direct contact with dry ice, as a safety measure. We then needed to create a light, and we improvised using a headlampand a can of beans.
We put this to the side of the jar with the dry ice underneath, Here’s how it looked inside after we put the jar with velvet and the lighting source inside an Amazon pantry box, with the whole thing on a cookie sheet for ease of carrying. We also put a doubled up towel underneath the dry ice to insulate it. Here’s how it looked inside the box with the light on. You’ll need to play with the lighting a bit. We used the rest of the velvet to make curtains so you can put your head inside the box forbest viewing.
Soon after the lid cools down you can see tiny droplets falling, like alcohol rain. You have to watch intently for awhile before you see this, but once you recognize this effect, you know it’s working. Every 15-30 seconds you’ll see a trail, which is a line of droplets that condensed around a particle of some kind. These lines fall at the same rate as the alcohol rain, so they disappear quickly. We’ve seen a handful of really visible ones but it’s not like a giant rainstorm of particles, just an occasional one. Timing is important for this. In 5-10 minutes after you place the jar lid on the dry ice it should be cold enough for the alcohol rain to start. After about 45-50 minutes the jar starts freezing up so best to get viewing in relatively soon after you’ve identified the alcoholrain.
In the Stong book they mentioned finding the little bit of radioactive material that exists in some old smoke alarms, which can in some cases lead to many more tracks if you place it near the chamber, but we didn’t have an old smoke alarm and so couldn’t try it. Because this was for a science fair where other kids got to see the project, my son made a safety sign: Kids will need to be careful not to touch the dry ice or the velvet. That’s the only big hazard here. Adults (or high school age kids) should also be the ones to pour the alcohol onto the sponge andvelvet.
My son Nicholas made a movie (big file, about 57 MB, MP4 format) about our efforts. It starts with a discussion of making the cloud chamber from a metal coffee can, an effort we abandoned because we ran out of time, but then it moves to the design on which we finally settled (we had two designs going at once, just in case). It might help you when making your own. Please forgive the “home video” nature of it, and our messy garage. It even shows the alcohol “rain” (but we didn’t capture any particle trails onthe video).
If you give this project a try, please email me to let me know how it workedout!
*
SunApr 5
2020
AN OLD (2012) STORY WITH LESSONS THAT ARE STILL IMPORTANT TODAY I had at some point bookmarked this 2012 article containing a story from BP about reducing greenhouse gas (GHG) emissions and saving money.
I’m posting it here now because the lesson it teaches is still important and relevant. BP thought its efforts to reduce GHG emissions would cost money, but instead those efforts generated a positivereturn.
Here’s the key paragraph: > “How could there be that much value available that was only > uncovered after the initiative to cut greenhouse gases, in effect to > use energy more effectively, and reduce emissions of gases such as > methane and halons? Simply put, almost everyone was busy with > other things, and not looking for these savings. And perhaps more > to the point, people had accepted a certain way of doing things that > was not optimal, but was the way they had been done for a very long > time. When you reset the context for the operation, which is what > the greenhouse gas target setting did, smart operators find a more > attractive solution.” I wrote about this general lesson in _Cold Cash, Cool Climate: Science-based Advice for Ecological Entrepreneurs_
back in 2012, talking about the power of the general approach of “working forward toward a goal”. In BP’s case, the goal was modest GHG emissions reductions of 10%, and setting that goal helped the institution realize possibilities it hadn’t seen before. This approach “frees you from the constraints embodied in your underlying assumptions and worldview” and prompts you to assess ideas that wouldn’t normally come up in the course of normaloperations.
Another insight is that the opportunities that arise from this approach are a renewable resource: > When I asked my friend Tim Desmond at Dupont whether his Six Sigma > team (which is responsible for ferreting out new cost-saving > opportunities across some of Dupont’s divisions) would ever run > out of opportunities, he said “No way!” Changes in technology, > prices, and institutional arrangements create opportunities for > cost, energy, and emissions savings that just keep on coming. Just because companies operate in a certain way doesn’t make it “optimal” for the current situation. There are always ways to improve operations, cut costs, and reduce emissions. We just need tolook.
Finally, it’s important to set such goals in the context of whole systems integrated design, in which we start from scratch to re-evaluate tried and true ways of performing tasks. Rocky Mountain Institute has for years championed the power of “Factor TenEngineering
”,
which allows us to create new ways of accomplishing the same tasks with substantial improvements in efficiency and emissions. For more on how to combine “working forward toward a goal” with “whole systems integrated design”, see Chapter 6 of _Cold Cash, Cool Climate: Science-based Advice for EcologicalEntrepreneurs
_.
Email me if you’d like a PDF copy of that chapter.*
FriFeb 28
2020
OUR ARTICLE ON CHANGES IN DATA CENTER ELECTRICITY USE FROM 2010 TO 2018, OUT IN SCIENCE MAGAZINE TODAY Our article on global data center electricity use is out today (February 28, 2020) in _Science Magazine_as a Policy Forum
article.
The intro of the article gives context: > Data centers represent the information backbone of an increasingly > digitalized world. Demand for their services has been rising rapidly > (_1_), and data-intensive technologies such as artificial > intelligence, smart and connected energy systems, distributed > manufacturing systems, and autonomous vehicles promise to increase > demand further (_2_). Given that data centers are energy-intensive > enterprises, estimated to account for around 1% of worldwide > electricity use, these trends have clear implications for global > energy demand and must be analyzed rigorously. Several oft-cited yet > simplistic analyses claim that the energy used by the world’s data > centers has doubled over the past decade and that their energy use > will triple or even quadruple within the next decade (_3_–_5_). > Such estimates contribute to a conventional wisdom (_5_, _6_) that > as demand for data center services rises rapidly, so too must their > global energy use. But such extrapolations based on recent service > demand growth indicators overlook strong countervailing energy > efficiency trends that have occurred in parallel (see the first > figure). Here, we integrate new data from different sources that > have emerged recently and suggest more modest growth in global data > center energy use (see the second figure). This provides > policy-makers and energy analysts a recalibrated understanding of > global data center energy use, its drivers, and near-term efficiency> potential.
Key findings:
> • Total global data center electricity use increased by only 6% > from 2010 to 2018, even as the number of data center compute > instances (i.e. virtual machines running on physical hardware) rose > to 6.5 times its 2010 level by 2018 (compute instances are a measure > of computing output as defined by Cisco> ).
>
> • Data center electricity use rose from 194 TWh in 2010 to 205 > TWh in 2018, representing about 1% of the world’s electricity use> in 2018.
>
> • Computing service demand rose rapidly from 2010 to 2018. > Installed storage capacity rose 26 fold, data center IP traffic rose > 11 fold, workloads and compute instances rose six fold, and the > installed base of physical servers rose 30%.>
> • Computing efficiency rapidly increased, mostly offsetting > growth in computing service demand: PUE>
> dropped by 25% from 2010 to 2018, server energy intensity dropped by > a factor of 4, the average number of servers per workload dropped by > a factor of 5, and average storage drive energy use per TB dropped > by almost a factor of 10.>
> • Expressed as energy use per compute instance, the energy > intensity of the global data center industry dropped by around 20% > per year between 2010 and 2018. This efficiency improvement rate > is much greater than rates observed in other key sectors of the > global economy over the same period.>
> • We also showed that current efficiency potentials are enough to > keep electricity demand roughly constant for the next doubling of > computing service demand after 2018, if policy makers and industry > keep pushing efficiency in their facilities, hardware, and> software.
>
> • We offered three primary areas for policy action: (1) extend > current efficiency trends by stressing efficiency standards, best > practice dissemination, and financial incentives; (2) increase RD&D > investments in next generation computing, storage, and heat removal > technologies to deliver efficiency gains when current trends > approach their limits, while incentivizing renewable power in > parallel; and (3) invest in robust data collection, modeling, and> monitoring.
Articles summarizing the work appeared yesterday in _The New YorkTimes
_,
_Bloomberg_
, _USA
Today
_,
_Data Center Dynamics_,
_Wired
_, _Quartz
_,
_IFL Science
_,
_New Scientist
_,
and _One Zero
_,
among other outlets. Google also did a blog post describing their progress in improving data center efficiency over time.
The Northwestern University news release is here.
The UCSB news release is here.
The Lawrence Berkeley National Laboratory release is here.
The spreadsheet model used for the analysiscan
be downloaded here: https://zenodo.org/record/3668743#.XmF-Gi2ZPWZ The full reference is > Masanet, Eric, Arman Shehabi, Nuoa Lei, Sarah Smith, and Jonathan > Koomey. 2020. “Recalibrating global data center energy-use> estimates
> .”
> _Science_. vol. 367, no. 6481. pp. 984.>
*
TueJan 28
2020
OUR ANALYSIS OF SUPERCOMPUTER EFFICIENCY OVER TIME Sam Naffziger of AMD and I just published our report on the efficiency of supercomputers over time.
Here’s the abstract: > The energy efficiency of computing devices is a topic of ongoing > research and public interest. While trends in the efficiency of > laptops and desktops have been well studied, there has been > surprisingly little attention paid to trends in the efficiency of > high-performance computing installations (known colloquially as > “supercomputers”). This article analyzes data from the industry> site Top 500
> (http://www.top500.org ) to > assess how the efficiency of supercomputers has changed over the > past decade. It also compares how the efficiency and performance of > a recently announced supercomputer, scheduled to be completed in > 2021, compares to a simple extrapolation of those historical > trends. The maximum performance of the most powerful supercomputers > doubled every 2.3 years in the past decade (representing a slowdown > from doubling every year from 2002 to 2009), while the efficiency of > those computers doubled every 2.1 years from 2009 to 2019. The Top 500 data have some issues, but this effort is a reasonable attempt to glean some meaning from them. We focused on analyzing each supercomputer based on the year that it started operation, so we could track meaningful technology trends. The Top 500 tracks the same supercomputers over time as they move down the list of top 500 machines, so we eliminated all but the first instance of any particular installation’s listing in the Top 500. We split analysis of the performance of supercomputers into two periods, 2002 to 2009 and 2009 to 2019. The 1st period shows rapid growth (doubling every year or so) while the 2nd period shows a much slower doubling time of about 2.3 years, as well as much great variance in the data. The efficiency data only start to become reliable around 2009, so that’s where we started the data analysis. Efficiency of supercomputers in the Top 500 data doubled every 2.1 years for the top performing machine, the top 10% of the top performing machines, and for the complete set of machines reported in the Top 500, which is pretty remarkable regularity. One caveat is that the R-squared of the linear regression goes down a lot as we regress on the bigger datasets.
We then focused in on the trend for the top performing machine so we could extrapolate that trend and compare it to an upcoming supercomputer (Frontier) built using Cray and AMD technology. The performance trend data show that Frontier is significantly above the trend line when it’s expected to start operation in 2021. The story is the same for efficiency, although Frontier’s height above the trend line is less dramatic than for performance. The details of how Frontier is expected to achieve these results are not yet public, but the article discusses some of the most promising areas for efficiency improvements as well as focuses on the need for future work, especially in the area of co-design of hardware andsoftware.
Koomey, Jonathan, Zachary Schmidt, and Samuel Naffziger. 2019. _Supercomputing Performance And Efficiency: An Exploration Of Recent History And Near-Term Projections_.
Burlingame, CA: Koomey Analytics.*
MonJan 27
2020
GO AHEAD AND WATCH A MOVIE ON NETFLIX! In October 2019, many news outlets (including Phys.org)
reported that watching half an hour of Netflix would emit the same amount of carbon dioxide (1.6 kg) as driving four miles. This appears to be yet another amazing “factoid” about information technology’s environmental footprint that has little relationship to reality. I dug into the calculations, at the prompting of the BBC, and figured out the real story. Half an hour of Netflix emits less than 20 grams of carbon dioxide, probably much less. The BBC interviewed me last week and did a nice story about it. Listen to the episode here(it is
the first story in the 28 minute show). Download a podcast version here (grab the one from Friday January 24, 2020, titled “Netflix andChill”).
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SunJan 26
2020
I DISCOVERED A HIDDEN GEM IN PALO ALTO TODAY The Museum of American Heritage is a fantastic independent museum in Palo Alto, California. We needed a short activity to pass the time (we were in Palo Alto for coding lessons for one of our boys) and I discovered this place online. Whata find it is!
The museum is a collection of artifacts from the 1800s and early 1900s, mostly gadgets of various sorts. It has a “general store” that uses the artifacts from one of the founders (whose parents owned a general store in the area until 1965). Some familiar brands are there if you look closely. Our boys had a go at dialing my phone number on an old rotary phone (they needed a hint). They had a real ice box! The big block of ice went in the upper left hand compartment and a bucket to catch melting water was in the lower right. Food went into the right hand compartment. Note the thickness of the doors. Well insulated! They also had an early 1900s fridge. Apparently it used freon and needed that big condenser on top. The compartment wasn’t very big, maybe 1.5 feet x 3 feet by 1 feet deep, if that. Also note the tinyfreezer.
For the kid set, the best features were the erector sets (not featured, but a source of endless fun) and the working old-stylepinball machine.
We also saw a cool bacon cooker! The fat drips off the rounded metal into the platter below. This looks like a gadget someone should make a modern version of now. Finally, we showed up on the same day as the Palo Alto “RepairCafe” in which
experts with tools help people who bring in their old appliances to get fixed up. It was quite a scene. It happens quarterly. This little museum vastly exceeded our expectations. If you are in the area, by all means give it a go. Here are the details: Museum of American Heritagehttp://www.moah.org
351 Homer Avenue
Palo Alto, California 94301+1-650-321-1004
Open from 11am to 4pm on Fridays, Saturdays and Sundays. FREE General Admission (donations gratefully accepted). For more details on the Repair Cafe, click here.
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WedDec 4
2019
DASHBOARDS OF KEY DRIVERS FOR THE RECENTLY RELEASED IEA SUSTAINABLE DEVELOPMENT SCENARIO (SDS) A couple of weeks ago, the International Energy Agency (IEA) released emissions scenarios related to the World EnergyOutlook
(WEO). We’ve used our spreadsheets to disentangle the drivers for those 2019 scenarios. We express the results in what we call a “dashboard of key drivers” (see below). The 2019 scenarios are based on IEA’s WEO model, which comes out every year. A related set of scenarios comes from IEA’s Energy Technology Perspectives model. You can read about both sets ofanalyses here
.
For more background, see the post on IEA historical drivers of energy sector carbon dioxide emissions here.
For our analysis of key drivers for the 2017 ETP scenario called “Beyond 2 Degrees”, go here.
The reference for our 2019 article upon which the decomposition analysis is based is at the end ofthis post. Email me
for
a PDF copy if you can’t get access otherwise. We also have an excel workbook all set up to do these decompositions and graphs, so let me know if you’d like a copy. Let’s first look at an equation known as the Kaya Identity, which describes fossil carbon emissions as the product of four terms: Population, GDP/person (wealth), Primary Energy/GDP, and Carbon dioxide emissions/primary energy. Over time, analysts have realized that this four-factor identity collapses some important information. That’s why, in our 2019 article, we moved to the expanded Kaya identity, with several moreterms:
The components of this identity are as follows: > CFOSSIL FUELS represents carbon dioxide (CO2) emissions from fossil > fuels combusted in the energy sector,>
> P is population,>
> GWP is real gross world product (measured consistently using > Purchasing Power Parity here and adjusted for inflation),>
> FE is final energy,>
> PE is total primary energy, calculated using the direct equivalent > (DEq) method (electricity from non-combustion resources is measured > in primary energy terms as the heat value of the electricity to > first approximation),>
> PEFF is primary energy associated with fossil fuels,>
> TFC is total fossil CO2 emitted by the primary energy resource mix,>
> NFC is net fossil CO2 emitted to the atmosphere after accounting for > fossil sequestration. For historical data, there is no sequestration of carbon dioxide emissions, so the last term is dropped in the historical blog post,
but included for future scenarios. Note that this identity applies only to carbon dioxide emissions from the energy sector. We use an additional additive dashboard for future scenarios to describe industrial process emissions, land use changes, and effects of other greenhouse gases, but IEA doesn’t report all those data, so I’m focusing just on the energy sector here. DISCUSSION OF FIGURE 1 (FACTORS) The first graph is what we call our _graph of key factors_, from the indented list above. In the first row we show each term in its raw form for both the reference case (in black) and the intervention case (in red). The second row shows indices with 2025 = 1.0. And the last term shows the annual rate of change in each term for reference and intervention cases. In each case, we plot historical trends from IIASA’s PFU database for each factor from 1900 to 2014 (in green dashed lines) and 1995 to 2014 (in blue dashed lines) The total fossil carbon is the end result of the other factors, which drive emissions. It grows modestly from 2025 to 2040 (when the new IEA scenarios end). This was unexpected for me (similarly to the 2017 ETS scenario ), and it suggests an area of fruitful inquiry (and comparison to other reference cases). I would have expected higher growth in emissions in a reference case, but it does indicate some minor (but insufficient) progress in reducing projected emissions growth. Population doesn’t vary at all between reference and intervention scenarios, which is commonplace for such projections. Population is not seen as a lever for climate policy except in rare cases, mainlyfor ethical reasons
.
There may be policies (like educating and empowering women) that we should do for other reasons, but almost never are these considered as climate policies (and that’s appropriate, in my view). Another observation about population emerges from these data also. Projected population growth to 2040 is slower than historical trends. This result mainly comes from long term changes that almost all demographers agree are underway, and this picture of slowing population growth is almost universal in long run energy scenarios. Unlike the 1971 to 2016 period, when population was responsible forhalf of growth
in energy sector GHGs, this driver will be far less important to emissions in the future. _Download higher resolution version of Energy Sector Factors for SDS_
Gross World Product (GWP) is another key driver, and that term is projected to increase by more almost three quarters by 2040 in both the reference and intervention cases. Final energy (e.g., energy consumption measured at the building meter or the customer’s gas tank) is projected to grow modestly in the reference case and decline modestly in the intervention case. Same for primary energy. Both grow much more slowly than historical trends, which is another interesting area of investigation. Fossil primary energy is roughly constant in the reference case and declines substantially in the intervention case. Same for total fossil carbon and net fossil carbon. Note the green line in the last column for the top two rows, where we plot the contribution of biomass CCS to net emissions reductions in the intervention case for comparison. Though these net emissions savings are often counted outside of the energy sector, they are linked to the energy sector and it’s useful to show their magnitude here for comparison. The last row of the dashboard shows annual _rates of change_, which reveal some interesting trends and suggests further investigations. Population grows at a modest but declining rate after 2030. Real GWP growth slows to 2040 but still remains above 3%/year by 2040. Final energy in the reference case shows modest but declining annual growth rates, while the Intervention case slightly declines over theanalysis period.
Primary energy growth rates decline for the reference case and increase for the intervention case (but only after 2030). This means that there are more conversion losses in the energy system over time after 2030 in the intervention case (because final energy growth rates are mostly negative during the forecast period for the intervention case). The effects of these different trends are modest, however. Fossil primary energy growth is modest over the reference case, but is negative (from -1% to almost -3%/year) for the analysis period. Total fossil carbon and Net Fossil Carbon show modest growth in the reference case and strong annual declines in the intervention case. DISCUSSION OF FIGURE 2 (RATIOS) The 2nd graph below shows _the expanded Kaya identity ratios_. Population is the same, but all the other columns show ratios from the 2nd equation above. Population and wealth per person (the first two terms in the Kaya identity) are the biggest drivers of emissions in the reference case, while the energy intensity of economic activity declines to offset some of the growth in the first two terms. _Download higher resolution version of Energy Sector Ratios for SDS_ The ratio of final energy to GWP tracks trends since 1995 for the reference case, and declines more rapidly in the intervention case. Why the rate of decline should become more strongly negative to 2030 and then rise again (leading to V-shaped curves for rates of change) is a question worth asking the modelers. As expected from the discussion above, the energy supply loss factor suggests losses are roughly constant in the reference case and grow by a tiny amount in the intervention case. The fossil fuel fraction (which shows switching from fossil fuels to alternatives) declines substantially over the analysis period, as does the carbon intensity of fossil energy supply (which shows switching _among_ fossil fuels). Interestingly, there’s a step change in the rates of change for the carbon intensity of energy supply in the last five years of the forecast, and it would be interesting to know from the modelers whythis comes about.
The last column shows the extent of carbon sequestration as well as carbon sequestration from biomass, which have a modest effect in the Intervention scenario. This column is measured as a fraction of total fossil carbon emitted, so some of the drop in this ratio is associated with declining absolute amounts of fossil carbon over time. Nevertheless, this graph indicates measurable but modest use of carbon sequestration (both conventional and biomass related) in thisscenario.
FIGURE 3: CARBON SEQUESTRATION IN THE REFERENCE AND SDS SCENARIOS _Download higher resolution version of CCS graph_ The effect of CCS in the SDS scenario is modest. The difference between TFC reference and TFC Intervention is the result of all factors other than sequestration, and then the tiny difference between TFC Intervention and NFC Intervention is the effect of CCS. We also put absolute reductions from Biomass and Fossil CCS below the zero line. The Biomass CCS effect is close to zero and Fossil CCS effect issmall.
Note that IEA does not release CCS results for the Intervention case for 2025 and 2035, only for 2030 and 2040. My colleague Zachary Schmidt fit an exponential curve to the 2030 and 2040 results and used the fitted curve to estimate CCS effects in 2025 and 2035 in theIntervention case.
This example illustrates the use of our decomposition dashboards for the 2019 2019 WEO results. It’s clear from this review that IEA needs to extend its WEO scenario past 2040, which I assume is in the works. It would also be interesting to compare trends in the final energy intensity of economic activity and the fossil fuel fraction to other studies to see whether the IEA projections could become moreaggressive.
REFERENCES
1. Koomey, Jonathan, Zachary Schmidt, Holmes Hummel, and John Weyant. 2019. “Inside the Black Box: Understanding Key Drivers of Global Emission Scenarios.” _Environmental Modeling and Software_. vol. 111, no. 1. January. pp. 268-281.Next »
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