Over the years, steady advances in CPU, memory, and networking technologies have made huge budget-friendly and scalable, high-performance data analytics possible. Pandas software can handle datasets of up to 100 GB pretty easily. Chunking functions can help you overcome any drawbacks to your RAM size.
But what if you need to run something that goes over 100 GB? If your data points run into the millions or billions, a fast CPU still wouldn’t fulfill your needs. Even if your CPU has 32 cores, you can still only process 32 data points at a time. Data scientists today are increasingly finding themselves in need of much more performance at reasonable speeds. Large CPU clusters are simply unaffordable, save a handful of corporations. So how do we get through this bottleneck? GPUs are a breakthrough solution. Read further to know more about GPU.
What is the GPU? And How Does it Work?
As the name suggests, GPUs are mini-computers explicitly built for graphics processing. A VRAM or Video-RAM is integrated into the motherboard to control the functioning of GPUs. GPUs are adept at processing vectors, light sources, textures, shapes, and geometries to produce realistic imagery. They work by leveraging the power of parallel computing rather than the sequential processing style used by CPUs – and that is what is exactly advantageous for data science. Let us understand this at a more fundamental level.
Imagine a CPU has 2 cores and 8 tasks to perform. 2 processes are taken simultaneously by the CPU, and multitasking is achieved. This is especially handy when you want to run multiple applications, extract zip files, or process spreadsheets. There are some things a GPU simply can’t do well.
On the other hand, a GPU has hundreds of cores. Some GPUs contain nearly 6000 cores, almost 200 times of what best-performing 32-core CPUs contain today! But every core is working on the exact same task, and they are optimized to perform repeated instructions in parallel – it is built in a correct manner for what you need when processing large data sets!
The above figure shows a comparison of performance between the latest NVIDIA GPUs and the best available x86 CPU. Note how GPU performance has scaled over time, now able to produce up to 7.5 TerraFLOPS (Floating Point Operations Per Second). Simultaneously, advancements in CPU technology have only been able to achieve up to 1.5 TFLOPS so far.
What is GPU Accelerated Analytics?
GPU accelerated analytics delivers a dynamic interactive analytics experience, wherein the parallel processing powers of GPUs are leveraged to accelerate processing-intensive operations such as data science and deep learning. It is not something new. In fact, Deep Learning has utilized GPUs for years now because they have huge cost benefits.
Here’s an example: Stanford created the world’s largest virtual brain, beating out Google’s corporate-backed data center in a small AI lab! And these were the results:
|Google Data Centre||Stanford AI Lab|
|Number of machines||1000||3|
|Number of CPUs/GPUs||2000||12|
|Power used||600 kW||4 kW|
In NLP, GPU-accelerated analytics can be used for processing large data sets like Exact phrases, AND/OR, Wildcards, Grouping, Fuzzy search. GPU databases can also be used in a complementary role as a fast query layer for Hadoop. The ultra-low-latency performance allows for use in applications that require simultaneous ingestion and analysis of a high volume and velocity of streaming.
How to Implement GPU Accelerated Analytics?
Programming for GPU takes a bit of practice; please look at the following are the fundamental steps to get you started on this time-saving and cost-saving journey:
- NVIDIA is very much the preferred choice compared to other GPUs like AMD. This is because NVIDIA themselves provide a lot of support for such implementations, and the community is much more prolific online. Make sure you have all necessary NVIDIA drivers installed and configured.
- For NVIDIA, the programming toolkit is called CUDA. CUDA stands for Compute Unified Device Architecture. It is made in C, but there are bindings in Python (PyCuda) and Java (JCuda). Install the appropriate CUDA toolkit for your system.
- To check if everything is functioning correctly, run the nvidia-smi command, and it will show you if your GPU is communicating with the drivers.
- Now you are ready to get started, ensure that you have a good understanding of AI jobs before you begin your venture in the field.
For more blogs on data science and cloud computing, checkout E2E Networks website. Also if you are interested in taking a GPU server trial feel free to reach out to me @ 7795560646.