Posts by bozz4science

61) Message boards : News : [TWIM Notes] Nov 17 2020 (Message 861)
Posted 19 Nov 2020 by bozz4science
Post:
Thanks for going into great detail in your weekly update.
62) Message boards : News : Badges! (Message 860)
Posted 19 Nov 2020 by bozz4science
Post:
Just wanted to ask whether the badges are implemented only for the CPU version or the combined CPU+GPU credits. As I crossed the 500k threshold a couple days ago, but still remain far below it if only CPU credit is counted towards it, I was just curious as the 500k badge hasn't been assigned yet.

Is the interval for badge assignment rather long or is only CPU credit counted towards the badge credit (either by accident or on purpose)?

Kind of curious either way, who will break the 500M score first? And how long it'll take for anyone to reach 1B.
63) Questions and Answers : Issue Discussion : All my GPU applications have crushed. (Message 849)
Posted 17 Nov 2020 by bozz4science
Post:
Any update on the potential Windows issue with MKLDNN? Still see the weird behaviour that only 1 out of 2 dedicated threads is used for each GPU WU but as soon as I scale it back to only use 1 per WU, the performance is suffering immediately. Would be great if you talk about this in you next update.
64) Questions and Answers : Issue Discussion : GPU tasks runtime comparison (Message 848)
Posted 17 Nov 2020 by bozz4science
Post:
I guess that's true – at least for now. Do you know if these lists are only analysing per WU runtime or can they also spot whether a user is running many WU concurrently on a particular card and thus change to another metric such as credit/hr per card? Just came to think about that as interpretation of the statistics would drastically change.

Edit to the 2nd runtime post: Just wanted to correct some mistakes I did in the wattage estimates. I forgot to count the energy requirement of the 2nd thread of the 2 WU in parallel option. The runtimes converged against ~1770 sec and thus I now get a slightly worse picture with 16.4W/WU and 3924.8 credits/hr
Running 3 WUs concurrently makes matters even worse efficiency wise while still increasing throughput slightly. Avg. runtimes are about 2610 sec, that result in 20.2W/WU and 3970 credits/hr.
At least for a very low power card, where avg. compute load was already high but not a 100%, offers potential to increase throughput but lowers overall efficiency as the "theoretical" compute load for 2 WUs in tandem would already push it beyond 100% of its compute capability resulting in inefficiencies arising from resource sharing/allocation, but load is now at 100% constantly.

Tradeoff to increase the throughput from 1 to 2 WUs concurrently by ~17% while raising power requirement by 13%. Anything beyond that doesn't offer any measurable benefit.
65) Questions and Answers : Issue Discussion : GPU tasks runtime comparison (Message 836)
Posted 13 Nov 2020 by bozz4science
Post:
Usually, I tend to consult the GPU comparison page in order to analyse how well my ancient card is still holding up against today's wattage beasts. https://www.mlcathome.org/mlcathome/gpu_list.php Surely, as more hosts and more hosts continue to crunch these GPU WUs, the comparison date becomes more accurate with time. However, it seems weird that the newer gen and more powerful RTX 20xx cards lack far behind much older cards. Could the Tensor cores of NVIDIA's RTX cards maybe be utilised for this?

For the sake of completion, I'd like to also see the 750Ti in that comparison list, but I guess according to the current layout, the table only lists the top 21 cards, right? F.ex. for the Milkyway@Home project, the comparison page lists all GPUs that is >30 models on their page. Could you expand the list or allow for more than 21 models to be listed?

Especially, given that I am among the top 20 user (GPU RAC) with only one 750 Ti, the numbers provided on this page, still seem to be out of proportion. Could you elaborate on this shortly?
66) Questions and Answers : Issue Discussion : All my GPU applications have crushed. (Message 835)
Posted 13 Nov 2020 by bozz4science
Post:
In my personal experience yes. You can take a look here (https://www.mlcathome.org/mlcathome/forum_thread.php?id=120) for more detailed runtime and efficiency comparisons. Currently I am running 2 WU in tandem on my 750Ti.
67) Questions and Answers : Issue Discussion : All my GPU applications have crushed. (Message 832)
Posted 13 Nov 2020 by bozz4science
Post:
Task Manager is reporting the load on the CPU core that is supporting the GPU
Thanks for the explanation, though I doubt that this is really what it shows. If this were true, it should show the same % of CPU util as the task manager shows in the corresponding process. And this is 8.6% which is roughly 1/12 out of the 6 hyper threaded cores. If this were to be the metric the GPU side panel window would show, it had to be the same 8.6% but is showing 5% instead...

I agree on the ~85% overall compute load on the 750Ti being reasonable. You could also try to run 2 units in tandem to increase load to 100% and efficiency as well.
68) Questions and Answers : Issue Discussion : All my GPU applications have crushed. (Message 830)
Posted 13 Nov 2020 by bozz4science
Post:
task managers shows 3% for my GTX750-TI. However GPU-Z shows 84% of "GPU load".
Still haven't figured out what exactly the task manager is reporting in Windows. But if you were to click the GPU monitor you get the different sub-windows showing load values for various GPU features (such as 3D engine, copy engine, memory, etc.) And you can change any sub-window to display some different component. By clicking on it and changing it to "Compute_0", you can let task manager display and monitor the CUDA compute load which then should be similar to readings of MSI Afterburner, CPU-Z or the like.

3% load for a GTX 750Ti sounds unreasonable, but I just checked on my system and task manager reports in the "main side bar" also only 5% load but the compute_0 window shows ~85% which seems in line with the readings from the other software.

#1: Intel i7-3930k, Win 7 Ult., 12GB ram, GTX1070 [445.57] ~3800 secs
#2: Ryzen R7-2700, Win 10 Pro, 32GB ram, GTX 1070 [445.75] ~900 secs
I don't know whether the OS is partially causing this discrepancy in runtimes, but again I have several ideas (yet don't know if it makes sense)
- RAM speed --> might partly explain the speedup of work on your R2700
- RAM type (ECC/non-ECC) --> might play a role as RAM data is quickly and constantly overwritten as intermediate weight updates and loss information has a very short half live (only 1 epoch)
- Total RAM capacity
- Overhead / daily use besides BOINC --> from what you wrote, I guess the main rig is running other stuff than BOINC in the background that could further impede computational speed
- Overclock settings of GPU / temp or performance preference
- PCIe slot width --> guess you are running single GPU setups only and thus all are in a x16 (gen3) slot
- PCIe slot gen
- Bus width --> your i7-3930k supports 5 GT/s but your Ryzen 2700 boast with 8 GT/s as far as I can tell which might make a difference in memory/CPU transfer and might be relevant as RAM is loaded quite heavily
69) Questions and Answers : Issue Discussion : GPU tasks runtime comparison (Message 827)
Posted 12 Nov 2020 by bozz4science
Post:
Thanks, I was just curious!

But I think you might be on to sth here. Appreciate your input! Could imagine that'd be part of the problem of ignoring the thread limits especially if it is notorious for this issue anyway. However, I still don't understand, how others and I come to observe this speedup as if more resources would be allocated but I cannot visually observe it. All remains unchanged for the GPU app WUs (CPU utilisation, active threads, bus interface load) except for the near instantaneous increase in the CUDA compute load in spite of the additional reserved CPU thread being untouched...

If it were to make a difference and could potentially solve the CPU thread allocation problem, I guess most of us would be willing to test this if you were to decide to push another 100 or so test WU.

I am fine with leaving this thread here, just thought it would fit better somewhere else, as this wasn't really an issue but rather just supposed to inform others. But as we might be on to sth here together I guess it's fine where it is.
70) Questions and Answers : Issue Discussion : GPU tasks runtime comparison (Message 821)
Posted 12 Nov 2020 by bozz4science
Post:
Sure. Definitely on the same page with you. Efficiency is top priority for me as well. Thus, finally, my curiosity was sparked enough, to try running 2 WU in parallel again and I succeeded by dedicating 2 CPU threads to each task. (However, still only 1 thread out of the 2 dedicated ones is loaded per task) Don’t know what to do with the results yet, as thinking about CPU resource allocation definitely is an issue with this setup. However, I see not only an increase in efficiency but also in throughput as seen below.

2 CPUs / 0.5 GPU
- 100% compute load (short bursts down to 92/94% after every epoch cycle)
- ø 25% bus interface load
- ø 63% memory load
- ø ~60% power load (peak at 70%)
- ø 5 % copy engine load (vs. 2% prior) --> more data handoff seems to be going on
- ø 1.5% 3D engine load (vs. <1% prior)
- same OC setting

Power consumption:
7.48 W (CPU) + 17 W (GPU) = 21.8 W (total)
--> W per WU = 10.9 W or 8.85 sec. per epoch

This compares to the best case scenario of 1,000 sec per WU like the following:
Runtime increase: 5.21 sec vs. 8.85 —> + ~70%
Efficiency increase: 14.75W vs. 10.9W —> + ~26% —> reasonable remembering that GPU compute load with single WU was around ~65%
Standardized throughput (assuming 959.40 credits per WU):
1 GPU task: ~691 epochs trained —> 3.60 WU —> 3,453 credits/hr @ 53.1W
2 GPU tasks in parallel: ~814 epochs trained —> 4.24 WU —> 4,065 credits/hr @ 46.2W

3 successful concurrent runs so far delivered:

Run 1)
WU #1 = 1,702 sec
WU #2 = 1,699 sec
∆ = 3 sec or 0.2%

Run 2)
WU #1 = 1,714 sec
WU #2 = 1,710 sec
∆ = 10 sec or 0.6%

Run 3)
WU #1 = 1,704 sec
WU #2 = 1,702 sec
∆ = 2 sec or 0.2%

It seems that the workload is not exactly evenly distributed between the available CUDA cores but indeed very similar. Thus, comparable runtimes for both WU in each run and very little variance. They seem rather robust with a std.dev of only ~5 sec. And all this with the card only running at 55C and 39% fan load.
71) Questions and Answers : Issue Discussion : GPU tasks runtime comparison (Message 819)
Posted 12 Nov 2020 by bozz4science
Post:
As the other thread was getting cluttered with posts about runtime comparisons of those tasks that actually didn't fail, I thought it would be great to share them in a dedicated thread. Let me share my experiences so far.
My system: https://www.mlcathome.org/mlcathome/show_host_detail.php?hostid=574
- CPU: Xeon X5660 @95W TDP
- GPU: GTX 750Ti@60W TDP
- OC setting: +120 MHz core + mem (1360 MHz core / 2820 MHz mem)
- OS: Win 10 (Dual Boot with Linux 20.04 LTS) --> gonna try this soon

In the wattage estimations, I forgot to include the power of the dedicated CPU thread(s).

1 GPU / 1 thread + 5 threads CPU tasks (MLC):
ø runtime: 1,250 sec
ø compute load (GPU): 65%
ø power load (GPU): 48%
Wattage per task: 10W (GPU) + 5.5W (CPU) = 15.5W (total)

1 GPU / 2 threads + 4 threads CPU tasks (MLC)
--> just one thread loaded for GPU task
ø runtime: 1,100 sec
ø compute load (GPU): 85%
ø power load (GPU): 58%
Wattage per task: 10.6W (GPU) + 4.8W (CPU) = 15.4W (total)

1 GPU / 2 threads + 4 threads CPU tasks (TNGrid)
--> just one thread loaded for GPU task
ø runtime: 1,030 sec
ø compute load (GPU): 85%
ø power load (GPU): 58%
Wattage per task: 9.95W (GPU) + 4.5W (CPU) = 14.5W (total)

1 GPU / 2 threads + 4 threads CPU tasks (TNGrid)
--> just one thread loaded for GPU task
ø runtime: 1,000 sec
ø compute load (GPU): 85%
ø power load (GPU): 62%
OC setting: aggressive +150 MHz core and mem vs. 120 MHz prior
Wattage per task: 10.35W (GPU) + 4.4W (CPU) = 14.75W (total)

0.5 GPU / 1 threads + 0.5 GPU / 1 thread (F@H GPU task) + 3 threads CPU tasks (TNGrid)
ø runtime: 1,550 sec
ø compute load (GPU): 99%
ø power load (GPU): 70%
Wattage per task not very intuitive: 16.8W (GPU combined) + 6.8W (CPU combined) = 23.6 W (total combined) (As the runtime vs. the normal 1 GPU/2 thread scenario is roughly 155% and the runtime of the same project F@H WU are between 2.5-3.0x times longer, I estimate that the GPU compute load distribution between MLC and F@H is roughly 65:35% and thus I get 15.35W for the MLC GPU WU)

Comparison of runtime vs. efficiency
1 GPU / 1 thread + 5 threads CPU tasks (MLC) --> 1,250 sec @ 15.5W / 6.51 sec per epoch
1 GPU / 2 threads + 4 threads CPU tasks (MLC) --> 1,100 sec @ 15.4W / 5.73 sec per epoch
1 GPU / 2 threads + 4 threads CPU tasks (TNGrid) --> 1,030 sec @14.5W / 5.36 sec per epoch
1 GPU / 2 threads + 4 threads CPU tasks (TNGrid) (higher OC) --> 1,000 sec @ 14.75W per epoch
0.5 GPU / 1 threads + 0.5 GPU / 1 thread (F@H GPU task) + 3 threads CPU tasks (TNGrid) --> 1,550 sec @ ~15.35W / 8.07 sec per epoch

rand WU - CPU version 1 thread @ 6 concurrent threads --> ~35,000 sec (rough estimate) @ 166.25W / 182.3 sec per epoch

Efficiency gain: ~ 166W/15W = 11x
Performance gain: 35,000sec/1,000sec = 35x


Took me quite a while to compile these estimates, especially as I had to wait for enough WU to finish and validate to have a representative average. Thought it'd be interesting to share these numbers with you and compare.

Just realized I posted this in the wrong forum but I cannot move it anymore to the Café section of the discussion forum.
72) Questions and Answers : Issue Discussion : All my GPU applications have crushed. (Message 818)
Posted 12 Nov 2020 by bozz4science
Post:
Very interesting input from you guys. I tried to further experiment with various settings and I strangely I saw a somewhat similar behaviour. It seems after setting up the app config file to reserve 2 CPU threads for the GPU task, the ø compute load increased from an initial 65% to 85% immediately after reading in the config file in the manager while a WU was running. That sped up things again for me and I am now (strangely again) very close to runtimes I have observed for GTX 970/980 cards with my 750Ti at around the 1,000 sec mark. However, and that's what is confusing me, upon the change to reserve 2 CPU threads for the GPU task, 1 CPU WU immediately gets suspended but the overall CPU utilisation of the GPU task stays at a 1-thread level only (overall CPU utilisation is reduced by 1 thread). So it seems that you were right about the overall # of CPU tasks running concurrently in parallel with the GPU tasks seems to impede its performance somehow. After all the GPU tasks demands around 3 GB of RAM on my system and loads my 2 GB GPU memory at ø ~40% on top of that.

I also saw a speedup depending on what CPU tasks were running. When running 5 out of 6 threads with MLC CPU tasks, for the GPU tasks I observed ø runtimes of 1,250 sec. When changing to other projects such as TNGrid, the runtimes improved to ø 1,100 sec. After continuing to running 2 threads on GPU task and 4 threads of TNGrid CPU tasks, I continue seeing the GPU tasks coming in at ~1,000 sec reliably.

I came up with other variables that might explain some performance discrepancies between different cards:
- RAM speeds
- RAM type (ECC or non-ECC)
- PCIe bus width
73) Questions and Answers : Issue Discussion : All my GPU applications have crushed. (Message 809)
Posted 12 Nov 2020 by bozz4science
Post:
If I were to see these kind of numbers on mine I would agree, however the GPU WUs come in reliably at the 1250 sec mark with little variance depending on which overhead task might be running in the background besides the other 5 out of 6 dedicated CPU threads. 80 min is very far off of my 20 min mark, so I really begin to wonder what might be the cause of this large discrepancy between 2 identical cards/card models. Like you pointed out, it might be a driver/OS combination, but I remain clueless.

Meanwhile I tried to load my GPU at 100% CUDA compute capability and managed to get there by running F@H units of the latest covid-19 sprint (project 13429) alongside the rand GPU WUs. These WU typically load the 750Ti's compute cap. only at 65%. Now I am still at ~60% power capacity. Each GPU task has a dedicated CPU thread and with a slight penalty of ~15% for the rand WU (1440 sec/epoch time 7.5 sec), and currently I see an estimated ~50% runtime penalty on the F@H tasks. I don't know if I will continue this setup but it remains to be seen if this is worthwhile if the penalty on the rand WU turn out to be really this small. It would still expedite my computational effort at this project by a factor of 24x.

And thanks for sharing this news!
74) Questions and Answers : Issue Discussion : All my GPU applications have crushed. (Message 806)
Posted 11 Nov 2020 by bozz4science
Post:
Don't be humble! What you accomplished here IMO is already a great accomplishment and proof of your relentless effort that definitely does speed things up quite a bit and at least for me results in a speedup of ~30x times compared to the CPU version. While my ancient 750Ti, as already discussed in an earlier thread, appears to be loaded rather high at ø 65% comparatively, (thus, I cannot really believe your single-digit compute load on your 750Ti) I can easily imagine the disappointment of someone with much more powerful hardware. If those numbers are true, they seem rather low and this means that the WUs never utilise the GPU compute power to its full extent. While this certainly is true, it also means that not much power will be needed to run the WU at such low utilisation but you’ll still see the speedup. I see however the reasoning behind wanting to utilise the compute power to the full extent Dataman and thus, why you would rather transfer your GPU compute power to projects such as F@H, GPUGrid, Einstein@Home or the like that tend to reach much higher utilisation and would probably do so myself if my CPU alone would deliver as much throughput as my current 750Ti :)

I will crunch away those rand WU 30 times faster than I did with a CPU alone. For efficiency comparison see my quick estimate here: (referring to CPU vs. GPU rand WU)
- CPU only: ~10.5 hrs on 1 thread out of 6 on a Xeon X5660 @95W gives an estimate of ~166.25W
- GPU + 1 CPU thread: ~1,250 sec @60W and an ø power load of 48% gives an estimate of 10W (= ~6.5sec per epoch)
Don’t know if I can do the calculation in the way I just did, but if this were true, these numbers are definitely an indication of the efficiency gain (~16x) of the GPU version over the CPU only version. I must admit Jim, that looks a bit too good to be true... And that is besides the 30x performance boost in runtimes. At least for low-powered GPUs.

I tried to run multiple WU in parallel but if the single WU compute load is over 50% already, my experience so far was that it would fail. And it did again. But I can easily forgive a compute load average of only 65% on my 750Ti given these aforementioned improvements.

I can easily imagine that future workloads, in which we would train multiple networks in parallel with different sets of inputs or hyperparameters, or in general more complex workloads such as RNN-WU will provide with experiment run 3, would largely increase the compute load. Anyway, I love the low power draw while still seeing the speedup but would still appreciate squeezing this 35% additional compute capacity out of the box. And letting 2 tasks compute simultaneously unfortunately doesn't work for me. From what I could read out of the stderr output it has to do with a memory error. At this fast pace of learning per epoch of only 6.5 sec I can easily see why memory bandwidth could impede a card's compute capability. My error was the following "- Unhandled Exception Record - Reason: Out Of Memory (C++ Exception)", though I am not sure of what caused it ultimately...
75) Message boards : Cafe : Motherboard / heatsink advice for AMD Ryzen chips (Message 798)
Posted 10 Nov 2020 by bozz4science
Post:
you can go with an AIO liquid cooling system

Thanks for pointing that out! Still looking into that, but I am not sure yet. So far I planned to go with a decent sized full tower case with great airflow, so I thought that a high-quality air cooler would do just fine as I am also eying the 3700X/3600 cpus that only run at 65W for which air-cooling hopefully should prove more than sufficient. Anyways, still speccing out everything and might as well consider an AIO.

Should I hopefully add a second rig in the future, I would definitely go with a higher TDP-rated CPU and then I'll go with an AIO!

I also turned off I think it was called PBO Boost. The CPU runs much cooler now.

That's great to know!
76) Message boards : News : [TWIM Notes] Nov 9 2020 (Message 794)
Posted 10 Nov 2020 by bozz4science
Post:
Great news! I got some GPU test WUs after all and they crunched away reliably in ~1,200 sec. The CPU equivalent runtime is on ø 35,000 sec, so for me at least that is a massive speedup. They were running from the get go without any problems. Also trying to suspend and continue WUs worked like a charm. The only 2 errors I received were "-529697949 (0xE06D7363) Unknown error code" so that wasn't helpful. The 2 errors were thrown only, while trying to rund 2 WUs simultaneously via specific app config settings. So I guess it was some internal memory corruption.

After reverting back to the stock settings for the GPU app, it works flawlessly again and at least for my CPU/GPU pairing, the speedup of ~30x is incredible. Testing was conducted on a Windows system with a Xeon X5660 and GTX 750 Ti.

Are you going to separate the GPU app version into its own respective application soon? And when are you planning to release the GPU WUs into the wild?

Thx
77) Questions and Answers : Issue Discussion : All my GPU applications have crushed. (Message 789)
Posted 10 Nov 2020 by bozz4science
Post:
Would be worth testing for this project. It is working OK but takes nearly 2 hours.

That just confused the heck out of me, but after looking at the stats of your host with the 750Ti right now, it seems the runtimes are very similar. Definitely a net improvement for me and thus, I'll keep my GPU busy here occasionally. – Interestingly the WU of your host with the 1060 is considerably faster than the one with the 2080Ti.

I have learned to go with Nvidia, as much as I like my RX 570s.

I've heard about the driver issues before, and earlier gen AMD Radeon cards were on top of that not very power efficient yet cheaper. Interesting read about NVIDIA's apparent problem with Samsung's 8nm process. So I guess, early adopters of the RTX 30xx series are really upset if they were "lucky" enough to get one in spite of the spares supply.

For low-power use, I have bought a couple of GTX 1650 SUPER

Honestly, they seem like a better value deal than the 1660 or 1660Ti. The 1660Ti's performance relative to the 1650 Super is ~130% and at 120W, at 120%, so power almost scales linearly with performance. However, price doesn't scale linearly. The price of a 1660Ti is usually 50-75% higher than for the 1650 Super but only for a 30% increase in performance, depending on the make and model of course. So there is a premium of 20-45% roughly on the higher-end 16xx series cards. That gives me some food for thought....
78) Questions and Answers : Issue Discussion : All my GPU applications have crushed. (Message 788)
Posted 10 Nov 2020 by bozz4science
Post:
Have you checked whether you ticked the option to receive test applications in the computing preferences? Otherwise I don’t see why it shouldn’t.
79) Questions and Answers : Issue Discussion : All my GPU applications have crushed. (Message 785)
Posted 9 Nov 2020 by bozz4science
Post:
My first rand GPU WU just finished crunching a few minutes ago at 1,218.45 sec. That is compared to roughly 10.5 hrs for the CPU version. That's roughly 31x times faster if they are the same length.
https://www.mlcathome.org/mlcathome/workunit.php?wuid=1259521
https://www.mlcathome.org/mlcathome/result.php?resultid=2796956
It was assigned to me after it threw an error almost immediately on 2 prior hosts, but mine finished without even a hiccup. GPU loaded on average at 75% (range: 62-79%), compute load at ø 72% (60-75%), power limit at ø 48% (42-54%), fans at 1299 RPM or 37%, mem load at 38% constantly, bus interface at ø 18% (16-19%), frame buffer at ø 15% (4-17%). Thought it'd be interesting to share. This is what I could read off the MSI afterburner log.

But anyway, gotta hand it to you. Yeah, it's painfully slow. That's why I consider the upgrade and am still looking into my options.

Edit: Just saw a couple GPU test WUs on your computers and somehow this seems wrong. Your 1080 Ti averaged ~1,350 sec for the GPU 9.75 Windows CUDA version. How or rather why? My next WU is right on track to deliver another ~1,250 sec. runtime. – It did finish indeed at 1,214 sec. So rather reliably and computed one epoch every ~6 sec.

Here's the second valid WU. https://www.mlcathome.org/mlcathome/result.php?resultid=2796261 For context. The GPU is overclocked at 1361 MHz core and 2820 MHz mem clock and paired with a Xeon X5660. But I still cannot figure out how mine could run at 1/4 of what your 750Ti delivered apparently .... Completely clueless here.
80) Questions and Answers : Issue Discussion : All my GPU applications have crushed. (Message 783)
Posted 9 Nov 2020 by bozz4science
Post:
GTX 750Ti [if it runs on this ancient card it will run on anything ;)]
I wouldn't write off a GTX 750 Ti altogether however. Even though it is arguably ancient technology, at 30$ a piece second-hand on Ebay and @60W TDP it still performs rather solid, and is a great solution for SFF cases or cases with bad airflow as the low TDP tends to keep the card on the cool end anyway. With the recent CUDA support that has been rolled out at Folding@Home on some cores, specific workloads such as the weekly sprint WUs finish considerably faster. It manages ~24 WUs a day and that equates to ~250k credits. On GPUGrid this card can also manage ~120k credits. I'd say this is still some science done at the end of the end while not being the most efficient card performance out there now. Compared to modern cards, that is of course nothing but I rather tend to look at projects that offer both a CPU and GPU app version for comparison and my GTX 750Ti always wins performance and efficiency-wise against the Xeon X5660 @95W that accompanies this CPU. Anyway, I just hope that the new RTX 30xx Ampere and new Radeon RX 6000 series cards will push prices of last generation cards further down. Still looking for a GPU upgrade myself but haven't figured out yet what offers the best value. RTX 2060/1660Ti cards seem rather affordable right now as do the lower end new Ampere cards. Don't know about the Radeon cards as I would likely miss CUDA for some projects.

Will run the first GPU WUs soon and I am already excited to see the results.



Previous 20 · Next 20

©2022 MLC@Home Team
A project of the Cognition, Robotics, and Learning (CORAL) Lab at the University of Maryland, Baltimore County (UMBC)