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| − | = Donating your idle computer time to a good cause =
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| − | I have been feeling [https://en.wikipedia.org/wiki/Altruism altruist] again, so I thought I could donate my spare computing power to some scientific project with a laudable goal.
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| − | I have tried the [https://en.wikipedia.org/wiki/Berkeley_Open_Infrastructure_for_Network_Computing BOINC platform] in the past and got disappointed, and this time was no different, except that I decided to write an article about it.
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| − | == Why contribute ==
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| − | Even if you are watching an HD video on the Internet, your computer is so insanely fast that is actually just sitting idle most of the time.
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| − | Millions of PCs, video consoles and mobile phones are wasting computational time right now. The prospect of using those combined global resources for some good cause at virtual zero cost is indeed tantalizing. Wouldn't you want to help those poor scientists full of good ideas but starved of cash?
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| − | == Is it worth contributing now? ==
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| − | Probably not.
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| − | Computing power is still exponentially increasing all the time, and most importantly, efficiency is still increasing at a great rate. That means that you are constantly getting more processing speed for less money and with a lower electricity consumption. Therefore, year 2014 is not the most efficient point in time to get started, at least if you consider the current environmental impact of producing energy.
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| − | Contributing now helps some project of your choice be the first to achieve its goals. Being the first does have some value, especially for the researcher. For some biological projects, having the results earlier could mean new treatments are available faster, which may be crucial for potential patients. However, this kind of computing-intensive projects are becoming easier all the time, so getting there is just a matter of time.
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| − | An increasing number of contributors should raise the general perception that distributed computing is important, which should make funding platform software improvements easier. However, it does not look like BOINC has been improving much in the past years (see below for more information on this). Therefore, participating now probably does not make a difference anymore, at least for that platform.
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| − | At a personal level, contributing is going to cost you some time. You will have to install software, choose a worthy project, create an account somewhere (yet another password to remember), and maybe ask your employer for permission first. You will have to learn a new program and, at least when using a PC, deal with the quirks described below. And there is also the increased electricity bill.
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| − | It is your call at the end of the day. I decided I wanted to contribute, but ended up writing this article instead. But the way it has turned out to be, I am not sure that this really counts as a contribution.
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| − | == Does it make sense to use volunteered distributed computing? ==
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| − | This is a human organisational issue. For most projects, the most economical solution at a global level would be to design hardware specifically for the task at hand. This does not have to be completely new hardware ([https://en.wikipedia.org/wiki/Application-specific_integrated_circuit ASICs]), as [https://en.wikipedia.org/wiki/Field-programmable_gate_array FPGAs] will often suffice.
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| − | Even if a project uses commodity hardware, a centralised computing centre that buys CPUs (or [https://en.wikipedia.org/wiki/GPU graphics cards] with computational functions) in bulk will probably achieve a much better value for money overall, especially regarding the electricity consumption. With the advent of [https://en.wikipedia.org/wiki/Cloud_computing cloud computing], this is getting easier and cheaper all the time.
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| − | The problem is getting the necessary funding. Society does not think as a whole, but each nation, goverment, institution and so on has a separate budget. Using volunteered processing power may help an individual project overcome funding difficulties. It is probably not efficient in global terms, but it does get the job done. After all, there is no good solution in sight for this kind of organisational problem.
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| − | == Choosing your good cause ==
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| − | You will be giving your processing time for free, so I would choose a project that makes its computing results public. I would also support those projects with [https://en.wikipedia.org/wiki/Open-source_software open source] computing software. Finally, I would also favor non-profit institutions.
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| − | For example, the [http://folding.stanford.edu/ Folding@home] project run by the Stanford University states in their FAQ page "following the publications of these scientific articles, we will make the raw data of the folding runs will be available to other researchers upon request" <nowiki>[</nowiki>[https://en.wikipedia.org/wiki/Sic sic]<nowiki>]</nowiki>. They also have a "Why don’t you post the source code?" FAQ entry which states "we stress that the vast majority of our code is already open source". Personally, I would expect all data to be public for everybody to use freely and all the code to be open-sourced from the start. Therefore, I would NOT choose a project with such restrictions.
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| − | There are many projects available on the BOINC platform, but little help about choosing one. The website only mentions what hardware and software platforms a project can run on, which falls short in my opinion. There should be a way to filter projects by the criteria mentioned above. For example, they could offer an option to sort the project list based on whether the computational results are kept private, made public, or published with restrictions (only on demand, just for certain scientist groups, or with some usage limitations).
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| − | If a project is listed in BOINC, it probably has passed some selection criteria, so it should be trustworthy. This is important, because you will be installing a managament software on your PC that automatically downloads binary executables from the Internet.
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| − | There are of course [https://en.wikipedia.org/wiki/List_of_distributed_computing_projects other distributed computing projects] that do not use the BOINC infrastructure. In fact, Folding@home is one of them.
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| − | I went for [https://en.wikipedia.org/wiki/SETI@home SETI@home], which is what got BOINC started. It may not be the best use of your computing time though.
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| − | == First impressions with BOINC ==
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| − | I tested version 7.2.42 (latest as of october 2014) under Kubuntu Linux 14.04 and under Microsoft Windows 7.
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| − | === You need to accept a software license ===
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| − | The first thing you'll notice when installing BOINC is that you need to accept the LGPL license. As a anonymous volunteer, you are probably not in the mood to accept any license. In fact, you do not actually need to accept this license in order to use the software. Section "9. Acceptance Not Required for Having Copies" of the GPL (which the LGPL is based on) starts with "You are not required to accept this License, since you have not signed it".
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| − | I am not a lawyer, but I suspect that the additional clause "Restrictions: You may use this software on a computer system only if you own the system or have the permission of the owner", while a valid warning about a potential, common-sense issue, is probably formally incompatible with the GPL, which admits no additional restrictions about how the software may be used. I guess the BOINC project could place this kind of restriction in their service usage policy, but if you think about it, it does not actually make sense.
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| − | === Replacing your screensaver by default ===
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| − | On Windows, the default installation will replace your screensaver. The BOINC markerting department is probably hoping to get some free advertisement space on your PC monitor. They could just suggest that you use the BOINC screensaver, because it is cool, or mention that it is "good" advertising and you may want to help spread the platform too. But making it the default, and having to press the "Advanced" button first so that you can untick that option, is rather cheeky. Definitely not the right way to treat your volunteers.
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| − | === Unnecessary system restart under Windows ===
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| − | On Ubuntu Linux, you do not need to restart your PC after installing BOINC. On Windows, restarting the PC should not be necessary either. Nowadays you can install and remove Windows services without a restart.
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| − | === Why you need a user account ===
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| − | The first time BOINC starts, you are given the chance to add a computing project. I selected SETI@home, and I was prompted to create a new account or use an existing one for that particular project. I do not know yet whether this applies to all projects, or if it is specific to SETI@home, but the log-in dialog box looked generic to all BOINC projects.
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| − | True altruism ist anonymous. Besides, I do not want to remember yet another login and password, and give my e-mail address away in the process. My guess is, once more, that this "requirement" is mainly for marketing purposes.
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| − | There is virtually no user-identification mechanism in place, so you can make up your name etc. when creating an account. The BONIC infrastructure does not really trust you anyway. Every piece of data is computed twice on separate volunteer computers, effectively halving the available processing power. This way, faulty computers or even malicious participants stand out quickly.
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| − | ==== Account scoring ====
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| − | Distributed computing platforms tend to implement some sort of scoring, so that your own contributions are visibly acknowledged. Some platforms are trying to promote a sense of "community" and offer different rewards as an incentive, like your name appearing on the "top 100" contributors list, or some special mention or price if your computer happens to be the one that finds some interesting computational result. When SETI@home introduced a competitive aspect, it even prompted attempts to 'cheat' the system.
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| − | There usually are statistics per hardware and software platform. Some companies have used this in the past in order to advertise their hardware, according to the motto "look at BOINC's statistics, we sell the fastest and most reliable computers". I would not trust this data anyway. These projects tend to be starved of money, and it must be hard to resist the temptation to cook the books in order to favour in the statistics some particularly-generous hardware manufacturer.
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| − | If you want to promote yourself or your company, you can claim that you are doing something good for society by helping some laudable project in this way. You can also add a link to the particular project, so that, when a visitor clicks on the link, he can see how much power you donated and that your are still contributing today.
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| − | While I do not mind this kind of corporate marketing, I do not see the need to participate myself. The computing software could just create some random ID on each PC, and display a message window if it really needs the user's attention. Having a user account and playing such social games should be entirely optional.
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| − | === Surprise! Surprise! ===
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| − | After choosing a project, computation begins. The first thing you will notice is that your CPU usage goes up to 100 %, which is a good sign.
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| − | The trouble is, the CPU fan will ram up to full throttle, permanently raising the noise level to maximum. And your electricity bill will increase more than you probably thought. As a volunteer, this is probably not what you bargained for. To top it all, your system performance may sink noticeably. Under Kubuntu Linux 14.04, I was getting compilation times twice as long as usual.
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| − | Of course, your mileage mary vary. You may have an Atom-based motherboard without any fans that always consumes little electricity, no matter what the load is. Or you may be running the Android client on a modest mobile phone processor only when charging overnight. Or maybe your OS copes better with the background load. But I would venture that the picture I painted above is the most common scenario.
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| − | Once you have become aware of the problems, if you look carefully at BOINC's documentation and start searching around on the Internet, the underlying problems will start to emerge. The BOINC documentation does not feature these issues prominently or thoroughly enough. New users are presented the buzzwords and the hype first, and are not given initial advice about what to expect. Again, this is no proper way to treat your volunteers.
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| − | == Power consumption ==
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| − | === About processor efficiency ===
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| − | Modern computer processors are like cars: they consume very little when idle and are most efficient at a certain speed. Above that speed, energy consumption increases uneconomically. Fan noise and heat increase much faster than the corresponding speed gain too.
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| − | If your computer is under load, the CPU and GPU are the components that consume most. For example, an Intel Core i7-2600 processor has a [https://en.wikipedia.org/wiki/Thermal_design_power thermal design power] of 95 W, and some other models go as high as 130 W. GPUs are much worse. Under heavy load, [https://en.wikipedia.org/wiki/Performance_per_watt performance per watt] becomes important.
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| − | Time is money, so, if you are waiting for the computer to finish a certain task, you may not mind the extra cost and the higher fan noise level. The same applies if you want the best possible gaming experience. But you probably do not want the extra burden if you are donating your "spare" computing power. Actually, there is no such thing as "spare" processing power anymore, for your processor will always consume much more electricity doing some actual work than idling in sleep mode.
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| − | === Global considerations ===
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| − | Electricity prices have increased in the past years, especially in Europe, mainly due to new regulation responding to environmental concerns. If you were to run your consumer CPU full throttle all the time, it would probably be more efficient to donate your increased electricity costs directly to your project of choice instead. For example, making your CPU consume extra 90 W for 6 hours a day in Germany will add around 50 € to your yearly bill (as of 2014).
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| − | Europe has been passing some legislation recently in order to increase power supply efficiency and limit the maximum amount of power a consumer PC is allowed to consume, but laws alone do not help much when running software like BOINC. The only places where the ratios between processing power, electricity consumption and heat generation are taken seriously is in dedicated computing centres, and they get to buy special server processors that are not available on the consumer market. Some people are running Bitcoin harvesters at home, and they do look at those ratios as well, but they tend to use special hardware too (mostly purpose-made GPUs).
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| − | === Operating system shortcomings ===
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| − | Computer processors were not always so frugal when idle, but they have been like this since many years ago. However, BOINC, Linux and Windows apperently have not realised yet. Power managament has seen many advancements for battery-powered devices like laptops and mobile phones, but donating spare CPU processing power does not seem to rank very high on the agenda.
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| − | The root of the problem is the so-called "race to sleep" or "race to idle" model of computation used by most consumer devices. This model optimises power consumption for consumer workloads and is not suitable for sustained background loads.
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| − | Microsoft Windows offers some power-managament settings buried in a big options tree under "control panel", "power management", "advanced". Linux has more internal flexibility, but tends to lack a user interface for the mere mortals. If you want to ajust even the most basic CPU power settings, you have to edit configuration files as the ''root'' user.
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| − | === Finding the optimal CPU speed ===
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| − | Finding the optimal CPU speed at home is all but impossible. There are just too many factors to consider. Energy consumption depends on the CPU family, model and exact submodel, and is normally not well documented. It also depends on the kind of load, on the current ambient temperature, on the system configuration (other components on the mainboard), and on your operating system's version number.
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| − | Even if you can determine the point of maximum efficiency, it may still increase your electricity bill more than your personal absolute limit, so you may want to reduce your contribution based on that anyway.
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| − | Here is some practical advice:
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| − | * If the fan noise bothers you, limit the load until the fan quiets down.
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| − | * Use an appliance energy meter to monitor consumption when idle and when under load. <br/> Adjust the load according to the extra electricity costs you are willing to pay.
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| − | * If in doubt, choose the lowest CPU frequency available. <br/> That is, assuming you can control it, see below for more information.
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| − | === Measurements on my home computers ===
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| − | For illustration purposes, I did a quick measurement on my home computers with an inexpensive, not-properly-calibrated meter:
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| − | * Laptop:
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| − | ** Specs:
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| − | *** Kubuntu Linux 14.04
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| − | *** Intel Core i3-380M CPU with 2 cores and Hyper-Threading, 2.53 Mhz
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| − | *** 8 GiB RAM DDR3-1066
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| − | **Consumption:
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| − | *** Idle consumption: 17 W <br/> The fan normally runs every now a then for a little while.
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| − | *** With SETI@home: 37 W (at 20 W a 118% increase) <br/> The fan runs at full speed and is therefore louder.
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| − | * Desktop PC:
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| − | ** Specs:
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| − | *** Microsoft Windows 7
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| − | *** AMD Phenom II X4 910e with 4 cores, 2.60 GHz (min 800 MHz), TDP 65W
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| − | *** 4 GiB RAM DDR2-800
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| − | *** ATI Radeon HD 7700 series passively cooled (without fan)
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| − | *** No case fan, just one CPU fan for the whole system
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| − | *** 26'' full HD flat screen
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| − | ** Consumption:
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| − | *** PC-only idle: 87 W
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| − | *** With monitor turned on: 134 W (at 117 W a 688% increase from the laptop) <br/> Part of the increase is probably due to the graphics card now doing some work.
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| − | *** With SETI@home, CPU only: 187 W (at 53 W a 40% increase)
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| − | *** With SETI@home using the GPU too: 208 W (at 21 W a 11% increase) <br/> The CPU fan got louder.
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| − | *** With SETI@home, GPU only: 168 W (at 34 W a 25% increase from idle) <br/> You cannot use the GPU only, so I set a 1 % CPU limit.
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| − | *** Gaming (GTA IV): 194 W (at 60 W a 45% increase from idle)
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| − | == Performance impact of running a background load ==
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| − | === ''Dynamic overclocking'' rendered useless ===
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| − | Running a constant background loads renders ''dynamic overclocking'' technologies like Intel's Turbo Boost or AMD's Turbo Core useless.
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| − | If dynamic overclocking is looking at the CPU temperature, it will always see a warm CPU, as it is constantly doing some work. If it is looking at the number of cores currently in use, they will all be busy all the time.
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| − | As a result, you may notice a small decrease in your PC's responsiveness.
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| − | You can certainly limit the background load in BOINC's "computing preferences" dialog, but then you will reduce your contribution to the project, and you will probably lose most of the dynamic overclocking advantages anyway.
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| − | === Shared processor resources ===
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| − | Some CPUs have shared resources between cores. For example, [https://en.wikipedia.org/wiki/Hyper-threading Intel's Hyper-Threading architecture] shares most of the CPU resources between 2 logical cores, and [http://en.wikipedia.org/wiki/Bulldozer_%28microarchitecture%29 AMD's Bulldozer architecture] has one floating-point core for each two integer cores.
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| − | Let's say a normal process is running on the first core, and a low-priority process runs on the second core, which would otherwise be idle. If the low-priority task is using a shared resource at the moment, and the normal process wants to use it too, it will have to wait, because the CPU hardware does not know anything about thread priorities, and most hardware-based operations cannot be interrupted anyway.
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| − | === Cache pollution ===
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| − | Nowadays, most CPU cores share a common on-die cache. Any background load, even if it is using just one core, will [https://en.wikipedia.org/wiki/Cache_pollution pollute the cache], as far as normal-priority threads are concerned.
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| − | Cache pollution is just another case of sharing a common resource, but it can have far more dramatic effects on performance, because a single CPU core can claim shared cache space very quickly. Say a normal-priority process is waiting for some I/O event or just accessing the same memory region in a tight loop for a short time. When this process attempts to access other memory regions later on, it will have to wait longer than usual, as the other concurrent, low-priority threads will have flushed most of the shared cache out in the meantime. Cache write-back buffers will also be busy with background processing data, and they have no knowledge of thread priorities either.
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| − | How much this affects system performance depends on the current load and on the OS [https://en.wikipedia.org/wiki/Scheduling_%28computing%29 scheduling policy]. If the foreground tasks do not run continously for long periods of time, but make short I/O pauses and/or trigger many context switches, the effects of cache pollution will be more noticeable.
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| − | If your CPU does not support partitioning its shared cache, there is not much you can do to prevent this issue. I haven't seen a modern consumer CPU with this feature yet.
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| − | For normal consumer loads, the performance impact should not be too high.
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| − | === Impact on foreground tasks ===
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| − | BOINC tasks run with the lowest-possible process priority, sometimes called 'idle' priority. Under Kubuntu Linux, the System Monitor shows for those processes a 'nice' level of 19 and a scheduling policy of 'batch'. Under Microsoft Windows, the Task Manager displays a "Low" priority.
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| − | In theory, BOINC processes should not have any effect on foreground tasks, because the operating sytem scheduler will never let a low-priority process run while a higher-priority one is waiting. At least that is what I have read in most documentation. In practice, life is a little more complicated, and there are I/O waiting times, variable scheduling time slices and temporary priority boosts. Therefore, a process' priority has some built-in dynamic components and does not always remain at its initial absolute value.
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| − | I already mentioned that I was getting compilation times twice as long as usual under Kubuntu, so I decided to investigate further. The compilation task consisted of a full rebuild of a smallish C++ project which included the regeneration of its [https://www.gnu.org/software/autoconf/ autoconf] configuration script. The ''autoconf'' part runs sequentially many small processes, and the compilation part runs in parallel, spawning one GCC instance per source file. Both build phases felt slower than usual when BOINC was running in the background.
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| − | Benchmarking an ''autoconf''/GCC project is difficult and inflexible, so [https://github.com/rdiez/Tools/tree/master/SyntheticTask I wrote a simple script] to simulate that kind of load. In order to minimize the effects of cache pollution, all processes run the same synthetic tasks, which consist of either an empty loop or a loop that echos a fixed string to ''/dev/null''. Each test finishes after a fixed number of iterations, and the elapsed wall-clock time is displayed at the end.
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| − | Factors to consider are:
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| − | * Number of parallel background processes.
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| − | * Background process priority, which is a combination of ''nice'' level and scheduling policy. SCHED_IDLEPRIO (chrt --idle) seems to be slightly better than SCHED_BATCH (chrt --batch).
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| − | * Type of background task (empty loop, "echo >/dev/null" loop, SETI@home/BOINC).
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| − | * Number of parallel foreground processes.
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| − | * Type of foreground task.
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| − | * Number of sequential child-process invocations in the foreground processes.
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| − | * OS scheduler, like Linux [https://en.wikipedia.org/wiki/O(1)_scheduler O(1)] or [https://en.wikipedia.org/wiki/Completely_Fair_Scheduler CFS].
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| − | * OS power manager configuration, like the Linux ''ondemand'' governor with optional setting ''ignore_nice_load''.
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| − | I actually did no proper scientific research, but just looked at a few scenarios. This is what I learnt: if you are running stable foreground processes, you do not lose too much performance to background low-priority processes. However, if you foreground tasks start many short-lived child processes, then performance suffers, as the low-priority processes end up getting a sizeable amount of CPU time, even though they actually should not. You can observe this behaviour with your system's process monitor tool. I do not know yet of any easy system configuration tweak to prevent it.
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| − | Check out section "Measurements on my home computers" above for the system configuration. Under Microsoft Windows, I was using the [https://en.wikipedia.org/wiki/Cygwin Cygwin environment].
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| − | This is the foreground task, with so many parallel processes as logical CPU cores. Each process executes many short-lived subprocesses sequentially:
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| − | Linux:
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| − | ./synthetic-task.sh $(getconf _NPROCESSORS_ONLN) 1000 30 echo_dev_null_loop
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| − | Cygwin under Windows (slower [https://en.wikipedia.org/wiki/Fork_%28system_call%29 fork support], so less iterations):
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| − | ./synthetic-task.sh $(getconf _NPROCESSORS_ONLN) 100 30 echo_dev_null_loop
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| − | Running the following long, parallel background task at the same time causes the workload above to take 140 % longer (that is, it takes more than twice the time) to complete on my Linux laptop, and 60 % longer on my Windows PC:
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| − | Linux:
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| − | chrt --idle 0 nice -n 19 ./synthetic-task.sh $(getconf _NPROCESSORS_ONLN) 0 100000000 empty_loop
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| − | Cygwin under Windows (no support for ''chrt''):
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| − | nice -n 19 ./synthetic-task.sh $(getconf _NPROCESSORS_ONLN) 0 100000000 empty_loop
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| − | Running a parallel SETI@home BOINC project in the background makes the foreground task take 155 % longer to complete on my Linux laptop and 70 % longer on my Windows PC.
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| − | The following foreground task does not run any child processes:
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| − | ./synthetic-task.sh $(getconf _NPROCESSORS_ONLN) 0 30000 echo_dev_null_loop
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| − | With this workload, performance impact is much lower. Running the low-priority synthetic task or SETI@home in the background yields 70 % longer execution times on my Linux laptop. On the Windows PC, the difference is small.
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| − | == TODO ==
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| − | TODO: Write the rest of the article.
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