Over the years, GPU computing has become more popular in high-performance computing environments.
A market research agency that monitors the HPC industry found that 34 of the 50 most popular HPC application packages, including all of the top 15 HPC applications, support GPUs.
In this article, we will look at what GPU computing is, how the GPU works in the HPC environment, and how the TotalView debugger helps developers deal with all the challenges that are found in these complex applications.
What Is GPU Computing?
GPU Computing Graphical Processing Units (GPUs) are used as CPU co-processors. It helps increase computing power in high-performance and complex computing environments.
GPUs have traditionally been used to improve graphics performance on computers. Nowadays, however, we see GPUs increasingly used for things other than graphics, such as data.
CPUs typically have four to eight cores, with a maximum of 32 possible, while GPUs can range from hundreds to thousands of smaller bodies.
Working together in large-scale parallel architecture, the two accelerate the rate at which you can process information.
How Does a GPU Work in HPC?
Multiple GPUs are added to each node in high-performance computing clusters to take advantage of GPU computing. It allows teams to increase what they can do to compute capabilities through GPUs.
Doing so, however, increases the complexity of the computing environment. The way GPUs process data and represent threads, as well as other notions, might provide difficulties for a developer, such as:
- Iterating back and forth between the CPU and GPU code.
- Checking data as it moves between the two.
- Develop hybrid MPI and OpenMP applications using GPUs.
- When utilizing a cluster with several GPU nodes, it is possible to debug many GPUs simultaneously.
Having a capable debugger available can help developers manage these challenges. It must be one that can support CUDA from multiple GPUs and the latest GPU development technologies such as NVIDIA.
GPU Computing With TotalView
This TotalView NVIDIA GPU is debugging the CUDA application in a computing environment.
In the central source area, you can easily set breakpoints on your host or kernel code at any time – before you start running your program, during the process, and so on.
- Viewing Process and Thread States
- Support For GPU Computing
Viewing Process and Thread States
As a GPU-enabled application runs, the code is run on the CPU and GPU and is processed through hundreds to thousands of execution threads.
Understanding where the code is running and which CPU or GPU kernel threads have hit breakpoints can be challenging when too many lines exist.
As the code runs, the Processes and Threads View on the left-hand side of the screenshot shows the processes and threads running on both the CPU and GPU in the application.
Developers can easily navigate any line, view its status and data, and control its process.
The View of processes and threads provides an overall display to deal with scale. You can use it to quickly understand everything that works in parallel and even to the level-of-detail threads within GPUs.
TotalView helps you gather this information to quickly find out the status of your code and the state of your GPU and drill down and check what’s happening at a particular location.
Support For GPU Computing
TotalView is the latest with the latest NVIDIA GPUs and CUDA SDKs and supports debugging CUDA applications on the Linux x86-64, ARM64, and IBM Power9 (PowerLE) architecture.
Total View can support complex environments such as those using different MPI-based configurations using different multi GPUs or a hybrid environment of MPI and OpenMP.
How to evaluate the processing power of a GPU
Well, you can try with artificial benchmarks. They will give you some idea. The Nvidia CUDA toolkit, for example, contains some programs that run on both CPU and GPU and can be used to compare how long it takes to run them on each platform. Is.
If you only need to do this using GPU data sheets, you can do this too. For example, this is my GeForce 9500 GS page.
You can find information on the number of processing cores there. Processing capacity is proportional to GPU frequency and base number.
Some cards also have a GFLOPS number. There was a document with a more uniform description for each card, but I can’t find a way to get to it yet. Maybe it’s in CUDA toolkit downloads?
- Overclocking
- Undervolting
- Repasting
- Upgrading
Overclocking
You should avoid this method if you are using a laptop as it usually does not have enough heat dissipation capacity to handle this process, and it can damage your system.
Overclocking means playing cards at clock speeds faster than existing cards. If your computer is cool enough for this process, you should try to overclock it.
Undervolting
This process is recommended for laptops. Lowering a GPU’s voltage is known as undervolting. It will limit the voltage range, help your GPU maintain a good temperature, and increase your laptop’s longevity.
Many people confuse undervolting with underclocking and think it will reduce their laptops’ performance, but the two are very different processes.
Repasting
Here’s what you can do to improve GPU performance on your laptop. Pasting occurs when you remove the thermal paste on your GPU and replace it with a new one.
It’s a selective process, so if you don’t believe in doing it yourself, you should seek help. This process requires the use of good-quality thermal paste.
Upgrading
Sometimes, even after trying everything, performance may not be enough for you. In this case, if you are on a desktop PC, you should upgrade your GPU.
Final Thought
It’s not always possible to boost your GPU’s performance using these methods alone, so you should consider upgrading your existing machine.
Overclocking and undervolting are well-documented topics with a plethora of resources available online.
Make sure you are looking for your specific model when doing this because there is no universal setting that suits them all. Now go on an adventure to analyze and improve your GPU for your needs.
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