Be it a data scientist or any machine learning expert; no one will deny the importance of large datasets for any successful machine learning (or ML) models. As the size of datasets keeps increasing, the performance scaling of ML models tends to hit a peak – before experiencing a performance bottleneck or lag.
A growing technology within the ML fold is Deep Learning, which has both practical and research applications across multiple domains. Based on the use of artificial neural networks (or ANNs), deep learning is widely used to make accurate predictions from large volumes of data. In short, Deep learning requires a lot of computational power – or the best of hardware configuration.
One such hardware component is either a traditional and reliable CPU (or central processing unit) or the advanced GPU (or graphic processing unit) technology. Which of these two hardware technologies performs better – when it comes to machine or deep learning models with heavy datasets? First, let us discuss these technologies in detail.
CPU and GPU, an Introduction
Since the 1970s, CPU units – introduced by Intel – have been the brain of any computer or computing device. CPU units have performed these operations without any speed/performance bottlenecks, be it any logical, computational, or input-output operations. Essentially, CPU technology was designed with a single core – to perform a single operation at any given time.
Gradually, with technological advancement and higher computational demand, we began to see dual (or even multi-) core CPUs designed to perform more than one operation at the same time. Most CPUs today are built using a few cores – and their basic design is still prime for solving a few complex computations—example, machine learning problems that require interpretation or parsing of complex code logic.
Like CPU, GPU is also used in any computer to process instructions. The significant difference being GPUs can work on multiple instructions at a given time, thanks to parallelization. Most GPUs are designed with many processing cores – with clock speeds that are much lower than those of CPUs. However, with its multiple processing, GPUs can parallelize calculations using many threads – thus increasing the rate of calculations that would take longer using the CPU.
GPUs have smaller sizes – but larger numbers of cores consisting of arithmetic logic units (or ALUs), control units, and in-memory cache. In other words, a GPU is a specialized processing unit with dedicated memory for performing floating-point operations – that were conventionally used in graphics processing. GPUs have also been in existence since the 1970s but were mostly restricted for gaming applications. It only gained mainstream popularity after NVIDIA released its GeForce line of GPU server products.
Initially used for graphical rendering, GPUs gradually advanced to perform advanced geometrical calculations (for example, for polygon transformations or vertical shapes rotation into 3-D coordinate systems). With the release of the CUDA technology in 2006, NVIDIA introduced parallel computing into GPUs, thus accelerating the speed of computing applications. CUDA use in GPU acceleration applications allows the sequential part of the application workload to run on CPU (for single-thread performance). In contrast, the compute-intensive part of the workload is run in parallel on thousands of GPU machine cores.
CPU or GPU – Which is Better for Machine Learning?
As processing units, both CPU and GPU are built for computations and calculations on neural networks. From a computational viewpoint, GPUs are better-suited due to their parallel computation capability. ML frameworks like TensorFlow are built to leverage multiple CPU performance, reducing computing time on various threads.
For most data science experts, CPUs are easy to access Windows Cloud or Linux Cloud servers. As an example, E2E Networks offers cloud service for running CPU intensive workloads across industry verticals.
When it is a matter of advanced neural networks, training of deep learning models is the most intensive on hardware resources. During the training phase, neural networks receive inputs, which are processed using hidden layers – with weights – that are continually adjusted to derive a prediction from the data model. For accurate predictions, weights are adjusted to locate patterns in the input data. This type of operation is commonly known as matrix multiplications.
For neural networks handling around 1,000 to even 100,000 parameters, you can use any CPU-based computer to train the data model to handle this data volume in minutes (or at the most, hours). On the other hand, neural networks handling over 10 or 50 billion parameters would take years to train using the CPU approach. This is where GPU processors can have a significant impact – in faster processing and reduced training time.
How do GPUs achieve faster training of deep learning models? Only through parallel computing that runs all the operations simultaneously, instead of one operation after the other. As compared to CPUs, GPUs allocate a higher number of transistors to ALUs and fewer transistors to caching or flow control. As a result, GPUs are better oriented for faster machine learning or data science models – whose speed can be enhanced by parallel computations. Next, let us look at a few crucial parameters in deep learning models where GPUs can make a difference.
When to use GPU or CPU for Deep learning?
Listed below are a few parameters in deep learning that should determine when to use either CPU or GPU:
- High memory bandwidth
Thanks to its higher memory bandwidth, GPU is a faster technology as compared to CPU. When you are using large datasets to train your ML model, you need high memory bandwidth from your processor. On the other end, CPUs consume more clock cycles to compute complex tasks because of sequential processing. GPUs are built with a dedicated Video RAM (or VRAM) memory for handling complex tasks – thus leaving the CPU memory for less-intensive tasks.
For instance, on an average, CPUs can provide a bandwidth of around 60 GB/s, the GeForce 780 GPU processor offers a bandwidth of over 330 GB/s, while the NVIDIA Tesla GPU offers a bandwidth of close to 300 GB/s.
- Size of the datasets
As mentioned before, data model training is resource-intensive and requires a large dataset. This, in turn, requires high computational power and memory allocation – which shifts the balance towards GPU processing. In short, the larger the dataset and computing power, the more advantageous is GPU as compared to CPU.
- Optimization
Task optimization is much easier to perform in CPU cores than in GPU cores. Although they are much lesser in number, CPU cores are more potent than their GPU counterparts.
Based on its MIMD architecture, CPU cores can work on different instructions. On the other hand, GPU cores are organized within 32 core blocks – and can execute the same instruction in parallel using its SIMD architecture. Additionally, parallelization in extremely dense neural networks is complex in GPU computing.
- Cost factor
On average, GPU-based compute instances cost around two to three times that of CPU-based compute instances. The higher cost is only justified if you are looking for 2-3 times more gains in the performance in GPU data models. For other applications, CPUs are always the better alternative thanks to its lower costs.
You do not need to run your ML instances on your hardware or server with cloud hosting services. Instead, you can rent an external server (for example, a virtual private server on Windows or VPS Windows). These services are charged at a low hourly rate (as low as INR 3.5). All you need to remember is to delet your cloud instance once you have completed the job.
Among India’s largest cloud providers, E2E Networks offers various cloud-based configurations – with hourly rates starting from INR 2.80 per hour.
Now that you are well-versed with the various parameters let us look at some real-life scenarios that you should know before going for the right GPU technology.
ScenarioGPU technologyWhen you need to work mainly on machine learning algorithmsTasks that are small or require complex sequential processing can be handled by CPU – and do not necessitate the use of GPU power.When you are working on data-intensive tasksThis can be implemented on any laptop with a low-end GPU processor. Example, a 2GB NVIDIA GT 740M or a medium-level NVIDIA GTX 1080 with 6GB VRAM.When you are working on complex ML problems that leverage deep learningBuild your own customized deep learning solution – or use a Windows cloud server from E2E cloudWhen you are managing larger and complex tasks with more scalabilityYou can opt for a GPU cluster or multi-GPU computing that are costly to implement. Alternatively, you can save costs by opting for a GPU cloud server (example, the E2E GPU Cloud from E2E Networks).
Next, let us look at some of the best GPU for machine learning applications.
Best GPUs for Deep learning
Be it any project, selecting the right GPU for machine learning is essential to support your data project in the long run. NVIDIA GPUs are among the best in the market for machine learning or integrating with other frameworks like TensorFlow or PyTorch.
Here are some of the best NVIDIA GPUs that can improve the overall performance of your data project:
- NVIDIA Titan
These series of GPU processors are best equipped to handle any entry-level deep learning project. These consumer GPUs are mostly used for lesser complex tasks such as data model planning or testing.
Among the best GPUs in this series is the Titan V designed for use by data scientists and researchers – and has performance levels that of data centre-grade GPUs. Based on the Volta technology from NVIDIA, Titan V includes Tensor cores and is available in Standard and CEO editions.
The other GPU from the Titan series is the Titan RTX used for most ML workloads.
- NVIDIA Tesla
The NVIDIA Tesla GPU series is best recommended for large-scale AI and ML projects and data centres. Designed for GPU acceleration and tensor operations, the NVIDIA Tesla V100 is one GPU in this series that can be used for deep learning and high-performance computing.
Another popular offering from the Tesla GPU series is the NVIDIA K80, typically used for data analytics and scientific computing.
- NVIDIA DGX
This is the top-of-the-level GPU series used for enterprise-level machine learning projects. Optimized for AI and multiple node scalability, the DGX series offers complete integration with deep learning libraries and NVIDIA solutions. If you are looking for an Ubuntu-hosted GPU, then the DGX-1 is the best choice as it is integrated with Red Hat solutions.
Conclusion
When it is a matter of running high-level machine learning jobs, GPU technology is the best bet for optimum performance. With cloud applications designed for high memory tasks, E2E Networks offers the best and cost-effective GPU solutions that cater to different customer requirements.
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