Is multi-GPU better than single GPU for deep learning?

If there are hundreds of thousands of training images or categories, then a single GPU will not be able to handle those tasks alone. In this case, multiple GPUs can be used together to achieve higher performance than if only one GPU was used .

How does multi-GPU work?

Multiple GPUs provide accelerated computing power for specific computational tasks, such as image and video processing and training neural networks and other machine learning algorithms .

How many GPUs to train GPT 4?

The Cost of Training GPT-4 OpenAI has revealed that it cost them $100 million and took 100 days, utilizing 25,000 NVIDIA A100 GPUs . Servers with these GPUs use about 6.5 kW each, resulting in an estimated 50 GWh of energy usage during training.

Is there any benefit to having multiple GPUs?

Advantages of Dual GPUs: Increased Performance : In certain scenarios, dual GPUs can indeed provide a substantial performance boost, especially in applications that are optimized for multi-GPU configurations, like 3D rendering, video editing, and specific games.

Can you use Nvidia and AMD GPU together?

You may have heard of dual GPU setups in the past, but combining AMD and Nvidia in the same system? Even if you're using two of the best graphics cards, that's always going to be tricky. However, it's now been done and benchmarked, and the results are shockingly good — with a few major caveats .

How to run training on multiple GPUs?

For each batch: GPU 0 reads the batch of data and then sends a mini-batch to each GPU. The up-to-date model is replicated from GPU 0 to each GPU. forward is executed, and output from each GPU is sent to GPU 0 to compute the loss. The loss is distributed from GPU 0 to all GPUs, and backward is run. More items...

How to run 2 GPUs at once?

Connect SLI/ CrossFire bridge Both graphics cards need to be connected via a bridge cable that usually comes with the graphics card or has to be bought separately. Only GPUs from the same series can be connected to each other. Finally, close the case and plug in your display ports to the primary GPU.

Which GPU is best for deep learning?

That's why we've put this list together of the best GPUs for deep learning tasks, so your purchasing decisions are made easier. MSI GeForce RTX 4070 Ti Super Ventus 3X. Editor's choice. ... Source: Nvidia. Nvidia GeForce RTX 4070 Super FE. ... Source: MSI. MSI Suprim Liquid X GeForce RTX 4090. ... Source: Nvidia. ... Source: Nvidia. ... See More. Mar 19, 2024

How many GPUs do I need for deep learning?

Also keep in mind that a single GPU like the NVIDIA RTX 3090 or A5000 can provide significant performance and may be enough for your application. Having 2, 3, or even 4 GPUs in a workstation can provide a surprising amount of compute capability and may be sufficient for even many large problems.

Is RTX or GTX better for deep learning?

For tasks involving machine learning and deep learning, RTX GPUs are generally the superior choice .

Can I run multiple deep learning models on the same GPU?

MMEs can now run multiple models on a GPU core , share GPU instances behind an endpoint across multiple models, and dynamically load and unload models based on the incoming traffic. With this, you can significantly save cost and achieve the best price performance.

Guide to running multiple GPU work units (2024)

What is this?
ATI/AMD users will notice that by default their GPU isn't loaded to 100% and NVIDIA users may have GPU's that are capable of more compute work. This allows you to manually set how many work units you want to crunch simultaneously on your GPU.

How does it work?
You create a file called app_info.xml inside the project folder of your BOINC Data folder (normally: C:\Program Data\BOINC\Data\projects\www.worldcommunitygrid.org). The contents of this file will determine:
- How many GPU WU to run at a time
- Which WCG projects to run

Intestested! Where do I start?
The basic steps to this are:

ThE_MaD_ShOt said:
1: Create new profile on wcg site with HCC only and make it default.
2: Attach the rig you want to crunch the app_info with to the profile
3: Uninstall Boinc/Wcg
4: Delete folder under program Data
5: reboot
6: Install Wcg Client from Wcg site.
7: Reboot
8: Attach to project and immediately set to no new task.
9: Shut down client
10: Add your app_info file
(Default Windows 7: C:\Program Data\BOINC\Data\projects\www.worldcommunitygrid.org)
(Default Windows XP: C:\Documents and Settings\All Users\Application Data\BOINC\Data\projects\www.worldcommunitygrid.org)
11: Restart Client
12: Allow new task
13: Reboot just because.
Click to expand...

GPU User settings
(Please modify these to suit your system. If in doubt, ask.)

Replace the number in <count>.5</count> according to how many GPU WU you want to run at same time:

.5 for 2 GPU work units
.33 for 3 GPU work units
.25 for 4 GPU work units

1 / Desired GPU WU Total = count
(example: 1 / 4 GPU WU = 0.5 coprocessor count)

This applies to both single and multiple GPU setups.

CPU User settings
If you want to run more GPU work units than you have CPU cores, change the <avg_ncpus>1.0</avg_ncpus> line to specify how much of a thread to use on average:

Total CPU Threads / Total GPU WU Total = avg_ncpus
(example: Dual core CPU / 4 GPU WU = 0.5 avg_ncpus)

This applies to both single and multiple GPU setups.

Multiple card setup

If you're using mixed cards by default BOINC uses the best one, and in some cases may not use all your GPU even if they're the same. To use more than one GPU in the same machine go to the BOINC data folder (normally: C:\Program Data\BOINC) and look for file "cc_config.xml". If it doesn't exist, create it but the contents should have the following:

Code:

<cc_config><options>[B]<use_all_gpus>1</use_all_gpus>[/B]</options></cc_config>

Templates
(Current HCC version: 7.05)
If you want to crunch all projects, you can use this link for reference information: http://www.xtremesystems.org/forums/showthread.php?283509-Working-app_info-files.
An example of this in use by Norton:

ATI/AMD GPU ONLY (No CPU work)

Code:

<app_info> <app> <name>hcc1</name> <user_friendly_name>Help Conquer Cancer</user_friendly_name> </app> <file_info> <name>wcg_hcc1_img_7.05_windows_intelx86__ati_hcc1</name> <executable/> </file_info> <file_info> <name>hcckernel.cl.7.05</name> <executable/> </file_info> <app_version> <app_name>hcc1</app_name> <version_num>705</version_num> <platform>windows_intelx86</platform> <plan_class>ati_hcc1</plan_class> <avg_ncpus>1.0</avg_ncpus> <max_ncpus>1.0</max_ncpus> <coproc> <type>ATI</type> <count>.5</count> </coproc> <file_ref> <file_name>wcg_hcc1_img_7.05_windows_intelx86__ati_hcc1</file_name> <main_program/> </file_ref> <file_ref> <file_name>hcckernel.cl.7.05</file_name> <open_name>hcckernel.cl</open_name> </file_ref> </app_version> </app_info>

ATI/AMD GPU and CPU HCC (HCC GPU and HCC CPU only)

Code:

<app_info> <app> <name>hcc1</name> <user_friendly_name>Help Conquer Cancer</user_friendly_name> </app> <file_info> <name>wcg_hcc1_img_7.05_windows_intelx86__ati_hcc1</name> <executable/> </file_info> <file_info> <name>hcckernel.cl.7.05</name> <executable/> </file_info> <file_info><name>wcg_hcc1_img_7.05_windows_intelx86</name><executable/> </file_info><app_version> <app_name>hcc1</app_name> <version_num>705</version_num> <platform>windows_intelx86</platform> <plan_class>ati_hcc1</plan_class> <avg_ncpus>1.0</avg_ncpus> <max_ncpus>1.0</max_ncpus> <coproc> <type>ATI</type> <count>.5</count> </coproc> <file_ref> <file_name>wcg_hcc1_img_7.05_windows_intelx86__ati_hcc1</file_name> <main_program/> </file_ref> <file_ref> <file_name>hcckernel.cl.7.05</file_name> <open_name>hcckernel.cl</open_name> </file_ref> </app_version><app_version><app_name>hcc1</app_name><version_num>705</version_num><platform>windows_intelx86</platform><avg_ncpus>1.000000</avg_ncpus><max_ncpus>1.000000</max_ncpus><api_version>6.13.0</api_version><file_ref><file_name>wcg_hcc1_img_7.05_windows_intelx86</file_name><main_program/></file_ref></app_version></app_info>

ATI/AMD GPU and GFAM (HCC GPU and GFAM CPU only)

Code:

<app_info> <app> <name>hcc1</name> <user_friendly_name>Help Conquer Cancer</user_friendly_name> </app> <file_info> <name>wcg_hcc1_img_7.05_windows_intelx86__ati_hcc1</name> <executable/> </file_info> <file_info> <name>hcckernel.cl.7.05</name> <executable/> </file_info> <app_version> <app_name>hcc1</app_name> <version_num>705</version_num> <platform>windows_intelx86</platform> <plan_class>ati_hcc1</plan_class> <avg_ncpus>1.0</avg_ncpus> <max_ncpus>1.0</max_ncpus> <coproc> <type>ATI</type> <count>.33</count> </coproc> <file_ref> <file_name>wcg_hcc1_img_7.05_windows_intelx86__ati_hcc1</file_name> <main_program/> </file_ref> <file_ref> <file_name>hcckernel.cl.7.05</file_name> <open_name>hcckernel.cl</open_name> </file_ref> </app_version><app> <name>gfam</name> <user_friendly_name>GO Fight Against Malaria</user_friendly_name></app> <file_info> <name>wcgrid_gfam_vina_6.12_windows_x86_64</name> <executable/> </file_info> <file_info> <name>wcgrid_gfam_vina_prod_x86_64.exe.6.12</name> <executable/> </file_info> <file_info> <name>wcgrid_gfam_gfx_prod_x86_64.exe.6.12</name> <executable/> </file_info><app_version> <app_name>gfam</app_name> <version_num>612</version_num> <platform>windows_x86_64</platform> <avg_ncpus>1.0</avg_ncpus> <max_ncpus>1.0</max_ncpus> <flops>3347548492.458962</flops> <api_version>7.1.0</api_version> <file_ref> <file_name>wcgrid_gfam_vina_6.12_windows_x86_64</file_name> <main_program/> </file_ref> <file_ref> <file_name>wcgrid_gfam_vina_prod_x86_64.exe.6.12</file_name> <open_name>AutoDockVina64.exe</open_name> </file_ref> <file_ref> <file_name>wcgrid_gfam_gfx_prod_x86_64.exe.6.12</file_name> <open_name>graphics_app</open_name> </file_ref></app_version><app_version> <app_name>gfam</app_name> <version_num>612</version_num> <platform>windows_intelx86</platform> <avg_ncpus>1.000000</avg_ncpus> <max_ncpus>1.000000</max_ncpus> <flops>3347548492.458962</flops> <api_version>7.1.0</api_version> <file_ref> <file_name>wcgrid_gfam_vina_6.12_windows_x86_64</file_name> <main_program/> </file_ref> <file_ref> <file_name>wcgrid_gfam_vina_prod_x86_64.exe.6.12</file_name> <open_name>AutoDockVina64.exe</open_name> </file_ref> <file_ref> <file_name>wcgrid_gfam_gfx_prod_x86_64.exe.6.12</file_name> <open_name>graphics_app</open_name> </file_ref></app_version></app_info>

NVIDIA GPU Only (No CPU work)

Code:

<app_info> <app> <name>hcc1</name> <user_friendly_name>Help Conquer Cancer</user_friendly_name> </app> <file_info> <name>wcg_hcc1_img_7.05_windows_intelx86__nvidia_hcc1</name> <executable/> </file_info> <file_info> <name>hcckernel.cl.7.05</name> <executable/> </file_info> <app_version> <app_name>hcc1</app_name> <version_num>705</version_num> <platform>windows_intelx86</platform> <plan_class>nvidia_hcc1</plan_class> <avg_ncpus>1.0</avg_ncpus> <max_ncpus>1.0</max_ncpus> <coproc> <type>CUDA</type> <count>.5</count> </coproc> <file_ref> <file_name>wcg_hcc1_img_7.05_windows_intelx86__nvidia_hcc1</file_name> <main_program/> </file_ref> <file_ref> <file_name>hcckernel.cl.7.05</file_name> <open_name>hcckernel.cl</open_name> </file_ref> </app_version></app_info>

NVIDIA GPU and CPU HCC (Both GPU and CPU Work units of HCC only)

Code:

<app_info> <app> <name>hcc1</name> <user_friendly_name>Help Conquer Cancer</user_friendly_name> </app> <file_info> <name>wcg_hcc1_img_7.05_windows_intelx86__nvidia_hcc1</name> <executable/> </file_info> <file_info> <name>hcckernel.cl.7.05</name> <executable/> </file_info> <file_info> <name>wcg_hcc1_img_7.05_windows_intelx86</name> <executable/> </file_info><app_version> <app_name>hcc1</app_name> <version_num>705</version_num> <platform>windows_intelx86</platform> <plan_class>nvidia_hcc1</plan_class> <avg_ncpus>1.0</avg_ncpus> <max_ncpus>1.0</max_ncpus> <coproc> <type>CUDA</type> <count>.5</count> </coproc> <file_ref> <file_name>wcg_hcc1_img_7.05_windows_intelx86__nvidia_hcc1</file_name> <main_program/> </file_ref> <file_ref> <file_name>hcckernel.cl.7.05</file_name> <open_name>hcckernel.cl</open_name> </file_ref></app_version><app_version> <app_name>hcc1</app_name> <version_num>705</version_num> <platform>windows_intelx86</platform> <avg_ncpus>1.000000</avg_ncpus> <max_ncpus>1.000000</max_ncpus> <file_ref> <file_name>wcg_hcc1_img_7.05_windows_intelx86</file_name> <main_program/> </file_ref></app_version></app_info>

Troubleshooting
Alternate method: If this setup doesn't work, an alternate method is described here: http://www.xtremesystems.org/forums/showthread.php?283512-How-To-run-multiple-BIONC-clients-on-one-machine-not-an-app_info-method

Driver crahses:
If you are having driver crahses, the following registry modification might prevent that:
(Source: Bun-Bun from XS)

Code:

Windows Registry Editor Version 5.00[HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Control\Watchdog]"DisableBugCheck"="1"[HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Control\Watchdog\Display]"EaRecovery"="0"

How many WU can my GPU handle?
This is hard to say. I am currently running 2 on my HD7770's. People with OC'd 6970's can do up to 6! Again, treat this as a test starting low.

Other useful tips:

ThE_MaD_ShOt said:
[Edited]Just be careful loading up the wu's. You only want to load the gpu to around 95%. If you load it to much you will start erroring out the wu's. Also make sure you have good case air flow as the card is going to steady be at 95 or so %. And as KieX stated use at your own risk.
Click to expand...

ThE_MaD_ShOt said:
If you recieve this error under messages:
10/21/2012 10:19:43 PMWorld Community Grid[error] App version returned from anonymous platform project; ignoring
Simply ignore it.
If you receive this error:
10/21/2012 10:19:43 PMWorld Community Grid[error] No application found for task: windows_intelx86 640 ; discarding
It indicates that you have not spell the name of the application in the app_info.xml correct or the application is missing in the BOINC\Data\projects\www.worldcommunitygrid.org map, if that's the case you can hit 'Reset project' under 'Projects' in the BOINC manager.
Click to expand...

Guide to running multiple GPU work units (2024)

FAQs

Is multi-GPU better than single GPU for deep learning? ›

Can I run multiple deep learning models on the same GPU? ›