In mobile, 62.7% of computation energy cost is spent on data movement [pdf]

https://safari.ethz.ch/architecture/fall2019/lib/exe/fetch.php?media=onur-comparch-fall2019-lecture6b-in-memory-computation-i-afterlecture.pdf

by bshanks • 7 years ago

200 157 7 years ago

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bshanks2• 7 years ago

One way in which the brain differs from conventional computers is that neurons appear to serve as both CPU and memory storage. Rather than having a few fast CPUs and a large bank of dedicated memory with a few busses (leading to "the von Neumann bottleneck" of data transfer between memory and CPU), the brain has many slow neurons each of which has many connections with other neurons.

So, in-memory computing may be more brain-like.

The brain appears to be very good at massive concurrency with low energy consumption, at the expense of slow serial computation and a high error rate.

varjag• 7 years ago

There is no CPU in brain, some analogies don't go very far.

mikorym5• 7 years ago

From a mathematical view on complexity, I don't see exactly the difference between memory and computation. I wonder what would be suitable definitions for the two concepts.

shele2• 7 years ago

You can make sense of the differences abstractly considering the band, the state register and the instruction table of a Turing machine.

wongarsu2• 7 years ago

Memory is the current state, computation is the permutation of state (your step function that takes you from one state to the next).

tel• 7 years ago

If computation is the process of spending effort to receive information, then memory is partially paid-down computation. There are lots of ways to blur these lines.

arcticbull• 7 years ago

This is actually reflected in modern FPGA devices. To your point, there's no material difference between "computation" and "retrieval" (they're just two different ways of mapping a set of inputs onto a set of outputs). FPGAs [1] are basically giant arrays of truth tables. These are implemented as look-up tables -- RAM -- paired with some dedicated, optimized, high-complexity logic elements like multipliers.

Could you implement a 16x16 bit multiplier (yielding a 32-bit result) as a RAM or ROM, I mean, totally. It would be a 32 bit address space where each entry is also 32 bits. That would take 16GB. You'd have the fastest multiplier in the West, though, net of propagation delay.

[1] From a combinational logic perspective.

eggie2• 7 years ago

neurons appear to serve as both CPU and memory storage

I'm not sure this is really certain. But that uncertainty is more because it's hard to prove that there isn't another means of recording memory than something we know.

There has been speculation that there might be some genetic or biopolymer based system in the hippocampus, but I'm not familiar with anything more than speculation on that topic.

eggie2• 7 years ago

It seems unlikely to me that biological systems wouldn't exploit digital (e.g. genomic/DNA/RNA) kinds of memory storage, given that the materials are there and the energy costs of maintaining and accessing that kind of storage can be fantastically low. Keeping memory alive in neural circuits would seem much more difficult. Surely that's happening to a large degree though. I'm just highlighting the possibility of an alternative basis for certain kinds of memory in the brain.

polyterative2• 7 years ago

Can it be that the neural structure itself is the memory? The weak/strong relationships maybe form the memories themselves, reconsructing a vivid image like seeds for procedural algorithms.

MayeulC• 7 years ago

While there might be other mechanisms at play, plasticity is a form of memory: If each synapse can store weights, read them, and change them, it could be seen as a memory.

For me, an analogy that is quite adequate in computer science would be LUTs in an FPGA: they are memory+compute units, and can be used as both.

To go a bit further, any memory could indeed be considered a computation unit, or vice-versa: consider a results cache, for instance. The difference I see between memory and computation is that memory accesses are, if not instantaneous, at least constant-time. If you want more precision, this can be computed: the necessary memory footprint is extended trough time as well as space (if you only use a results LUT, you'd have to make it bigger to gain precision).

amelius• 7 years ago

Yes but in a computer every processing unit can be used for widely different tasks. In contrast, the neurons in a brain are used for the same task over and over again.

didericis• 7 years ago

the neurons in a brain are used for the same task over and over again

How does this square with the fact that people can get large portions of their brain scooped out and still retain many of their previous abilities?

This is a naive assumption, but if there were purely a 1 to 1 correspondence between a neuron and any given task, I’d assume you wouldn’t be able to recover from massive brain damage. But it seems like people can retrain the functional parts of their brain after damage to other parts to pick up the slack somehow.

mojuba6• 7 years ago

But you could as well say, the same part of computer's memory where the execution binary is stored is used for the same thing again and again.

I don't think these parallels between the computer and the brain are valid though. The brain is a highly parallel but slow machine, we have very fast and not-so-parallel computers (just a few cores). I think a lot of what the brain does can be expressed on computers without changing the architecture much, i.e. you don't need to compare the architectures, just the outcomes of computation.

KoftaBob• 7 years ago

Super fascinating, any recommendations on where I can do further reading on this?

Richard_East• 7 years ago

Low energy consumption checks out - a brain uses only 12 watts.

lm284694• 7 years ago

You have to feed and train it for the first 15+ years before it's any good though.

anticensor• 7 years ago

Your brain uses 12 watts at rest and 120 watts under stress.

tehjoker2• 7 years ago

High error rate?! I said precisely moving several complex muscles to vocalize a concept that requires simulating not only how I perceive it but how both the directed receiver and other listeners will as well while focusing my eyes rapidly on an area the size of a quarter.

LeifCarrotson• 7 years ago

Try doing that a couple times in a row, recording the audio and video. Can you do it the same way twice, with a measure of "same-ness" that can be trivially computed? No, humans cannot do that.

Error rate in the context of computing does not need to mean invalid, just different. Our astonishingly high abilities to compensate for variations in input data do not result from an ability to produce output data repeatably.

SilasX3• 7 years ago

Could it be feasible to use the "brainlike" model for practical hardware and reap benefits from avoiding the vN bottleneck?

My intuition is that such a system would be much harder to reason about -- and therefore harder for compilers to emit efficient machine code for -- but I'm assuming someone here knows the topic pretty well?

(Before you give me "the lecture", yes, I'm aware that in general it's not a good idea to simply mimic the kludges that evolution came up with.)

lm284694• 7 years ago

Unless we made some big advances I'm not aware of, as far as I know we really don't know much about how the brain works. I doubt anyone can answer your question.

ggreer• 7 years ago

The reason brains work the way they do is because evolution hill climbed to a solution, not because that solution is globally optimal. Compared to silicon, neurons are absurdly slow. Axons transmit information at 100 meters per second. Electronics transmit information 3,000,000 times faster. When your switching speed is 100Hz, the only way to get anything done in a reasonable amount of time is to be massively parallel. That's why brains are the way they are.

Other points of comparison: Animal wings (birds, insects, bats) combine both lift and thrust, but we don't fly around in ornithopters. And while animal legs combine support and power for locomotion, we don't drive around in vehicles with legs. Wheels are far more efficient, but they're not something that evolution can discover easily.

__s• 7 years ago

There's a few animals that have evolved wheels. Much harder to evolve an axle

kbenson• 7 years ago

Wheels are far more efficient

Wheels are far more efficient if you have a very specific type of terrain to deal with. It's not immediately obvious to me that a wheel is more efficient overall on naturally occurring terrain. For example, wheels don't work well on steep and rough terrain, or in water (as opposed to under water). The trade-off might be better stated as wheels are very efficient for very select terrain types, and that efficiency tapers off fairly sharply for some terrains, reaching 0% in some cases, while legs are less efficient overall, but achieve some level of usefulness on almost all terrains (making them a better choice for organisms that have disparate terrain types to deal with).

Similarly, a computer may be much faster in some types of operations, but we can't even get it to do some stuff that's fairly trivial for many animal brains, so that may also be a trade-off in efficiency somewhere (possibly in an area we don't sufficiently understand yet).

perl4ever4• 7 years ago

I would take exception to the idea that evolution can't/didn't discover wheels.

https://en.wikipedia.org/wiki/ATP_synthase

Everything from E. Coli, to yeast, to plants, to humans, is based on rotating molecular motors.

Now, it is true that we don't see macroscopic wheels on living organisms. But a person or animal is composed of trillions of cells. By analogy, we don't see the millions of vehicles in a nation composing an entity that has wheels made up of vehicles.

alecco2• 7 years ago

Slide 3. Paper https://people.inf.ethz.ch/omutlu/pub/Google-consumer-worklo...

Still, this doesn't sound right. Screen should be dominant.

segfaultbuserr• 7 years ago

I expect baseband hardware to be dominant as well.

marcosdumay2• 7 years ago

The paper is focused on "computation", that seems to be defined as what happens on the CPU and the things directly connected to it, and ignores everything else.

I clicked expecting a paper about 4G networking.

willvarfar• 7 years ago

I don't think they are talking about the phone's total energy; the charts in that excellent paper you linked to have costs for CPU, L1, L2, DRAM etc but no screen, GPU, radio etc.

A long time ago I was a technical product manager for, among other things, this stuff. At the time, the screen completely dominated and the GPU was a distant second, unless you were playing a 3D game, in which case it used almost as much as the backlight. CPU and radio were a long way behind them.

My intuition is that this hasn't changed, as I can still get dramatically better battery life on my new iPhone in iBooks by playing with the brightness slider.

alecco2• 7 years ago

Yes, the title of this thread is not clear.

lonelappde2• 7 years ago

Per Carroll's results in An Analysis of Power Consumption in a Smartphone: The backlight is either the lowest power local component (10%, 40mW) or highest (50%, 400mW) depending on how bright it is turned up.

The GSM radio is the highest power component (about 400mw), when it is working continuously (phone call).

Related: a big contribution of smartphone OS optimizations and stuff like wifi 6 is about being smarter about turning off the radio for micro time slices when not needed.

Laptops are similar, but it depends a lot on whether you are using a 15W CPU Ultrabook with no GPU, or 65W mobile workstation CPU plus a GPU.

Veedrac2• 7 years ago

A major improvement to the energy cost of data movement will come from closer memories, like HBM and on-die stacked DRAM.

pdimitar• 7 years ago

What do you mean by "closer memories"?

Veedrac2• 7 years ago

Putting memory closer to the CPU, through dense and short-distance interconnects, allowing for much more efficient communication. Eg. https://www.youtube.com/watch?v=-besHp8HLxo.

extropy• 7 years ago

TLDR: let's put compute inside the memory. I'm very leaning towards calling this bullshit.

Closer to memory compute is what we have been doing for last 10 years, and that's why we have ever increasing hierarchical caches.

Yes the ALUs are a tiny part of the power budget because moving / syncing data is the hard problem. If you want massive parralelism, use a GPU.

The in memory clone and zero could make some sense, but generally you want to start writing to that memory soon after that, meaning you still need to pull it into your cache and the benefit is negated.

marcosdumay2• 7 years ago

If you want massive parallelism, use a GPU.

The GPU doesn't have all of your data stored in it. The clone and zero part have a very weak reasoning, focusing on bad targets because they look better than important ones, but take a look at the next section, where they do a simple database search.

imtringued• 7 years ago

PIM is already a reality it's just a matter of time until it sees broad adoption. PIM does not suffer from the crippling limitations of GPUs which only perform well with arithmetic bound problems, cannot run conventional non SIMD code efficiently and need a relatively large batch size.

For instance try deserializing a million JSON strings with a GPU. The end result is a graph like memory structure which GPUs usually struggle with. GPUs cannot take advantage of sharing the instruction stream here because it will hit branches for almost every single character and diverge quickly. And finally if you only want parse one JSON object then the GPU is worthless.

A PIM based solution would not struggle with heterogeneous non-batched workloads with arbitrary layout at all but still offer the same performance advantages that GPUs enjoy compared to CPUs and reduce energy usage at the same time.

kstenerud6• 7 years ago

It's not just a problem with data movement inside the machine. Data movement between processes and machines is also horribly wasteful.

Text encoding/decoding wastes massive amounts of energy, which is why I've been developing a format over the past 2 years with the benefits of binary (smaller, more efficient) as well as text (human readability/editability): https://github.com/kstenerud/concise-encoding

aidos• 7 years ago

Seems like a good effort. I couldn’t easily find an example of the text encoding looking at your readme though.

Reelin3• 7 years ago

Text: https://github.com/kstenerud/concise-encoding/blob/master/ct...

Binary: https://github.com/kstenerud/concise-encoding/blob/master/cb...

Reelin3• 7 years ago

How does this compare to MessagePack? (https://github.com/msgpack/msgpack)

kstenerud6• 7 years ago

MessagePack:

* Doesn't have a sister text format for human editing.

* Uses raw uncompressed types for integers and floats, which wastes space

* Doesn't support arbitrary sized/precision numeric types

* Timestamp format uses seconds instead of Gregorian fields (which means that your implementation must keep a leap second database, and time conversions are complicated)

* No time zones

* Doesn't have metadata, comments, or references

* Container types have an unnecessary length field

* Stores data in big endian byte order (an unnecessary expense since all popular modern chips are little endian)

* Wastes an extra bit for negative integers

* 32-bit limitations on length (so max 4gb payload sizes)

* No versioning of documents

satanspastaroll3• 7 years ago

Waste can be very subjective.

Exchanging text makes the format easier to debug and accessible to humans, which can be a purpose in itself.

kstenerud6• 7 years ago

100% agreed, which is why Concise Encoding has 1:1 type-compatible binary [1] and text [2] formats, which can be automatically converted when needed with no data loss.

Human accessibility is an absolute must for any modern format.

[1] https://github.com/kstenerud/concise-encoding/blob/master/cb...

[2] https://github.com/kstenerud/concise-encoding/blob/master/ct...

9c8675a8• 7 years ago

It should only be easy to debug and accessible to humans when needed IMO. I really think it should be possible to load a "production message", log event or whatever into a debug parser which only then spits out human readable output.

userbinator• 7 years ago

I've worked with lots of binary formats, and they are equally "human-readable" (and far less unambiguous in a lot of cases) once you get used to it. As a coworker once said, "everything is human-readable if you have a hex editor."

It seems your format is yet another attempt at reinventing ASN.1.

kstenerud6• 7 years ago

But the point is that binary formats shouldn't require looking at hex data just to make sense of them. Especially not nowadays. Once you're stuck parsing binary data by hand (which is tedious), you've lost the amazing benefit that text formats gave us. And editing binary formats is even worse.

That's why I split it into compatible text and binary formats. You transmit/store in binary, and read/edit in text, and the computer deals with the conversion automatically.

Const-me• 7 years ago

Data movement between processes and machines is also horribly wasteful

I agree, and in .NET that’s a solved problem: http://const.me/articles/net-tcp/ (2010).

Their text format is XML. Their binary version takes small fraction of bandwidth, especially when using a custom pre-shared XML dictionary. Binary is much cheaper to produce or parse, the built-in DataContractSerializer understands both formats. Supported by all modern editions of .NET including .NET Core and is compatible across them, e.g. I’ve been recently using it for a network protocol of a Linux ARM device.

kortex2• 7 years ago

This is pretty cool! I really like that dates (and timezone info) are first class types. Often neglected, time ser/de has been a monkeywrench in many a project.

Protobufs are neat but `protoc` is an absolute nightmare. I would warn against going down the path of transpiling to lang-specific message files if at all possible (I don't think you are taking that approach, but github freezes my phone browser, I'll look more on my dev machine)

Great stuff! I would love to see this get more traction. Interchange formats are a classic victim of critical mass necessity.

aaxa• 7 years ago

I think this headline is very misleading. This is about computational energy cost and does not represent what the headline implies (that it's 62.7% of total energy consumption)

brohee• 7 years ago

Yeah on mobile screen and radio dominates the compute part by quite a bit. Not that it's not still worth optimizing.

lonelappde2• 7 years ago

Screen and radio are both data movement tools, so I don't understand the OP's classification process.

ksec• 7 years ago

Yes, It is just lower prioritises.

100% of the Mobile Phone Energy Usage during Internet Browsing, 50% goes to Display, 30% goes to Radio Transmission and 5G Encode / Decoding. Your Three SoC + NAND + Memory only uses 20%, I just think title is a little misleading.

Not to mention the Software we are running could be anywhere between 2 to 10x from maximum efficiency. It is just the human cost involves to get to the point might not be worth it.

There is also Moore's Law, while we might be near or already at the end of it, we still have few more nodes to go and could reduce the energy usage of SoC + NAND + RAM by 30% to 50%.

While on the Display and Radio is isn't so clear how far we could further reduces it.

bshanks2• 7 years ago

I agree, sorry. The original paper used the phrase "total energy" but although the paper text does mention powering the screen, their figures just talk about computational energy, so that is likely what they meant. It's too late for me to edit the title, but it should be "62.7% of computational energy".

dang• 7 years ago

Ok, we've clarified that in the title above.

pjc50• 7 years ago

Including a copy of Maslow's hierarchy of needs and some green field vs factory slides seems to be .. overselling it a bit?

Fortunately it pulls back towards a "what are the easy wins" approach: in-memory initialising (rather than stream zeroes over the bus, ask the memory to zero itself) and in-memory copy.

The three real problems are:

- this is only useful if the whole stack "knows" about it, or can be transparently optimised to use it. Otherwise the person running a Javascript for loop to set values to zero will ruin it.

- this relies on the data being in DRAM-row-size chunks, and moving it around within the same physical chip. That may also require re-architecting to make that common enough to be useful. Locality issues are the downside to distributed systems; the whole CPU architecture is oriented towards continuing to pretend that everything happens in a defined order in a single place.

- possible security issues (rowhammer?): may seem far off, but if you don't think about it upfront it gets very expensive later.

(Worth recapping: the silicon is fundamentally differently-processed for DRAM, such that implementing complex logic is slow and expensive.)

m0zg• 7 years ago

The entirety of acceleration hardware industry is based around this fact. Basically every single accelerator tries to move more memory towards significantly simpler (and wider) compute. At the extreme of this is compute-on-DRAM, not a new idea certainly, but one that is yet to materialize. Systolic architectures are also very efficient . GPUs are far less so, their main advantage is relatively good tooling and extensive programmability, not energy efficiency, per se. And CPUs utterly and completely suck at high throughput workloads, power efficiency wise. They do often have enough compute to do e.g. lightweight deep learning though.

Years ago it cost 8pJ/mm to move a byte on-chip. It's probably closer to 5pJ/mm now, but ALUs consume a fraction of this energy to do something with that byte. And once you leave the chip and hit the memory bus, things get _really_ slow and expensive.

euroPoor• 7 years ago

ethz going _strong_ these days on HN

mojuba6• 7 years ago

Some big part of the data movement can be eliminated by introducing persistent RAM, i.e. the memristor that never happened unfortunately. When the missing component is finally invented I think we'll have an opportinity to revisit some of the core OS concepts, such as a file. Just imagine for a second you have a persistent random-access memory device that doesn't require you to serialize/deserialize things, or move and resolve the binary modules. Your binary module/app will be executed right where it's stored, provided that it was resolved and prepared when it was first copied to your file system. Similarly, a JSON file will be stored as a memory structure rather than serialized JSON (converted to text as necessary, e.g. when viewed in a text editor) Etc. etc.

There are some really interesting changes that can potentially happen when the memristor type of memory becomes available, possibly with its own problems too, but with the huge benefit of moving less data.

wongarsu2• 7 years ago

Similarly, a JSON file will be stored as a memory structure rather than serialized JSON

You can do this in C++ right now: mmap a file to a memory region, and just create structs in that region.

This has two problems:

- normally you want to save at controlled points in time, otherwise you have to worry about recovering from states where some function updated part of the data and then crashed (phone ran out of power etc)

- just writing a bunch of internal data structures into a file used to be moderately popular and has great performance, but it's a major headache when you ever update them. You end up implementing a versioning scheme and importers for migrating old files to your new application version. At that point a file format that is designed for data exchange is less headache.

In general mmap already offers you a way to treat your disk like memory with decent performance (thanks to caching), and the number of good use cases turned out to be somewhat limited. I doubt just making that faster with new technology will change much.

jacquesm5• 7 years ago

There is a huge difference between what the GP proposes and your interpretation: the GP talks about a structure that is not just a memory backed copy of the data in the file (which still requires an in memory structure pointing to the bits and pieces to make sense of it, or a painful parse step for every access and an impossibility to write back to it).

So the whole structure in its native form rather than the contents of the json file, for instance a graph would reside in memory and could be operated on directly.

Hence the 'serialized' in the part that you quoted. Once you serialize it the whole thing becomes hamburger and needs to be parsed again before you can operate on it.

Iv2• 7 years ago

I recently realized that mmap was how good editors are able to open huge files without stalling like bad editors do. There are use cases for it but yes, you need to know what you are doing.

p_l• 7 years ago

Using `blitting` from and to disk for performance is how we got DOC & RTF split - with the former as "Work-In-Progress" format that was fast to save on a floppy, and the latter intended as interchange format.

History turned otherwise, unfortunately...

golergka• 7 years ago

There's already an experimental OS that is built on idea of persistent program memory: http://phantomos.org/

mojuba6• 7 years ago

That's very intriguing, thanks!

IshKebab• 7 years ago

There's 3D XPoint

https://en.wikipedia.org/wiki/3D_XPoint

aitchnyu• 7 years ago

My pet concern is "have you turned it on and off again?" will no longer work. Just like apps have a robust database and a frontend that needs restarting after getting into a bad state, programs for the Persistent RAM generation may need a robust state management system so that frontend state can be rebuilt.

taffer• 7 years ago

This could be solved with transactions: If all processing takes place within transactions, then you can always return to a clean state after a crash.

dmead• 7 years ago

The memristor is a real thing, just hasn't been implemented yet right?

magduf• 7 years ago

Basically, yes: some prototypes have been made in labs, but haven't really seen the light of day. Wikipedia has an article about it here: https://en.wikipedia.org/wiki/Memristor

As noted in the article, HP says that memristor memory may be commercially available by 2018, so I guess we'll see then....

zitterbewegung• 7 years ago

We have persistent ram technology . Intel and Micron have made ssd form factor and also DIMM form factor Optane devices. They are based on 3D xpoint. Putting it on an SOC is possible but would be very expensive at this time .

Gravityloss• 7 years ago

Most of the data is useless anyway. Bloat factor over mostly text content and an adequate quality picture is huge, probably 10x.

aberforth1234• 7 years ago

A small part of the solution: Let's block ads! Proof: https://webtest.app/?url=https://www.wowhead.com

the_pwner224• 7 years ago

That link was shockingly fast. I was quite surprised by how quickly it loaded - you don't notice any loading time unless you specifically look for it. If only everything would be like that...

egdod5• 7 years ago

Disable JavaScript. Everything becomes fast (or doesn’t work at all).

egdod5• 7 years ago

That actually might be the largest part of the solution.

aberforth1234• 7 years ago

Cool, let's do this!

halflings• 7 years ago

That's a pretty extreme example.

Here are the results for NY times: https://webtest.app/?url=https://www.nytimes.com/

Barely drops page size or power usage.

danShumway2• 7 years ago

How is energy consumption measured?
For now this test only displays the estimated Watt Hour for transferring the bytes of the web page (source). Data transfer is not all it takes to run ads, there is also data crunching happening on servers and rendering of the ads on clients. This means the estimated Wh could actually be higher, but I need more sources for that. Please email me![0]

Given that the page takes an extra 3 seconds to load with ads, power usage is probably higher in reality. On the data side, if you look at the screenshot for both pages, you'll also notice that the blocker-free Chrome version has a giant white space at the top where a banner should be.

My guess is that the banner hasn't loaded yet, but webtest didn't know to wait for it.

In my own (very, very unscientific) test, I loaded up NYT in Firefox (which has my standard extensions installed) and in a fresh Chrome install. The Chrome install downloaded 2.9 MB for ~170 requests, and my Firefox install downloaded ~240 KB for 17 requests.

I left the both browsers open for about a minute, and an extra .3 MB got downloaded on the Chrome side as part of a tracking ping, so it's not just page load either that's a concern here -- every minute you stay on the page you'll leak more data.

To be clear, the NYT is great at optimizing page load with ads. If anything, I would consider them to be a positive anomaly. But even so, I'm seeing ~80% bandwidth savings over a fresh Chrome install. I suspect my extensions are more aggressive than uBlock Origin is by itself. But the page still loads and works fine for me in Firefox, nothing appears to be broken.

[0] https://webtest.app/?url=https://www.nytimes.com/#energy

Enginerrrd• 7 years ago

Wow, this website is cool and makes for a fun and very interesting (and telling) comparison between old reddit and the redesign:

https://webtest.app/?url=https://www.reddit.com

vs.

https://webtest.app/?url=https://old.reddit.com

An order of magnitude increase in page size and energy consumed, and it takes 5 times as long to load, with 5 times the ad requests.

MisterTea• 7 years ago

Given the current environmental state, there should be a big push to shame these websites for wasting energy. Perhaps if this happened on a larger scale we'd see better designs produced that don't rely on highly wasteful and inefficient virtual machines that are mistook for web browsers.

noneeeed• 7 years ago

I knew that the new reddit site was terrible (and keeps getting worse), but just under 15mb for a website is insane.

kevas• 7 years ago

Just tried this using YouTube as the test site...w/ uBlock, makes more requests, larger page size, etc...hmm

aberforth1234• 7 years ago

That's because there is more room for thumbnails in the right column because there is no ad on top. Weird right :). We have to block more!

doppelganger27• 7 years ago

Just a guess, but from the preview image the site shows, youtube w/o ublock loads 2 large banners, and one row of videos, vs 3 rows of videos w/ ublock. The larger number of video thumbnails shown may explain the larger size

mirimir• 7 years ago

Wow, 97% of salon.com is ads!

Page size: 1.86 MB vs 0.05 MB

Energy used: 0.109 Wh vs 0.003 Wh

https://webtest.app/?url=https://salon.com

localhost• 7 years ago

Cerebras's "wafer scale engine" [1] takes some of these ideas and applies them narrowly to deep learning training. 400,000 cores, 18GB of on-chip memory, 9.6 petabytes of memory bandwidth in a 1.2 trillion transistor package in a gigantic 46,225mm^2 package.

[1] https://www.cerebras.net/cerebras-wafer-scale-engine-why-we-...

jedberg• 7 years ago

The number one cost of any distributed system is data movement.

Mobile phones are essentially leaf nodes in a huge distributed system, where they interact with "downstream dependencies", ie. the servers that provide all the functionality of their apps.

So it makes sense that moving data would use the most power.

stokedmartin• 7 years ago

"We observe that data movement between the main memory and conventional computation units is a major contributor to the total system energy consumption in consumer devices. On average, data movement accounts for 62.7% of the total energy consumed by Google consumer workloads." [0]

[0] https://people.inf.ethz.ch/omutlu/pub/Google-consumer-worklo...

colechristensen• 7 years ago

With all of that ad overhead it's reached a point where the actual costs incurred by the user rival the ad revenue gained by the publisher.

In mobile, 62.7% of computation energy cost is spent on data movement [pdf]

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