Big Data, Data Analytics, Predictive Analytics, Data Mining and Fuzzy Logic – These are the buzzwords of the 21st-century!
As the staggering amount of data being generated and collected increases exponentially, the business rationale for extracting market insights from all such data points is crystal clear. It has become critical for businesses’ survival to intelligently process the humongous amounts of collected data and subject it to concise, discerning analytics.
One way to achieve this is by harnessing the sheer computational power of Graphics Processing Units (GPUs). In this article, we’ll look at how GPU computing can make Big Data processing faster and more accessible.
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Leveraging the Power of GPU Computing
GPUs have proven to be excellent for applications like Artificial Intelligence (AI), Machine Learning/ Deep Learning (ML/DL), Data Analytics and High-Performance Computing (HPC) because they can quickly and accurately perform a substantial number of parallel calculations on a very large cross-section of variables and data relationships. This they effortlessly achieve through an unparalleled “Single Instruction, Multiple Dataset (SIMD) architecture” that takes advantage of the hundreds of highly-efficient Tensor and CUDA cores embedded in them.
No wonder GPUs have gone far beyond just rendering graphics in advanced games! Key reasons for relying on GPUs for Big Data processing include –
- Efficient and Cost-effectiveness – By harnessing the power of GPU computing, organizations and researchers can accelerate large-scale data processing and perform highly complex scientific/ graphical simulations in a fraction of the time taken by traditional CPU-based systems. This delivers significant time and cost savings and promises the ability to tackle computational problems that would otherwise be insurmountable.
- Access to Pre-defined Libraries – Several libraries and frameworks, such as CUDA and OpenCL, allow programmers to easily write code that can take advantage of GPU computing. These libraries constitute a simplified programming model for working with GPUs and provide access to a wide range of optimized algorithms and pre-defined functions for tasks such as matrix operations and Fourier transforms.
- Access to CUDA/Tensor/Python Programming Frameworks – In addition to algorithms and libraries, many popular Deep Learning frameworks like TensorFlow and PyTorch have built-in support for GPU computing. This empowers data scientists and researchers to effortlessly deploy GPU computing in their computation workloads without having to fret over the monotonous minutiae of GPU and CUDA-level instruction programming.
- Ability to handle Data Analytics, GNNs and ML/ DL training – Deploying GPUs for heavy computing can empower organizations and researchers to achieve breakthrough results by resolving complex computational problems, processing colossal datasets and visualizing highly sophisticated simulations quickly and efficiently.
- With the advancement of GNN- supported ML/ DL modules, the capability to process millions or even billions of data points with infinite number of interdependencies and relationships between them has not only become essential, but even deterministic to the survival of tech-savvy enterprises reliant on Big Data, for e.g. Social Media enterprises, eCommerce firms, Logistics conglomerates, etc.
- Energy-efficient – Using a single GPU is more energy-efficient than running entire arrays of CPUs to undertake the same quantum of work. Yes, this sharply reduces expenditure, but more importantly it is also an enviable opportunity for businesses to flaunt their green credentials – a virtue in modern economics where Environmental, Social, and Governance (ESG) policies and investments take center-stage.
Thanks to their ability to handle numerous calculations simultaneously, GPUs have emerged as potent tools for Big Data processing. Furthermore, not only do businesses speed up their data processing applications, but also succeed in reducing their operating costs manifolds since a single GPU server can undertake data processing equivalent to multiple CPU-based systems. Shifting to a Cloud GPU resource instead of opting for on-premise deployment can further improve financial efficiency by leveraging OpEx funds instead of CapEx outgo.
Experience Lightning-fast Computing Power with Cloud-based GPU Resources
Unlocking the Potential of Big Data with GPUs
GPUs today constitute a robust data processing system that can handle extensive data querying quickly and generate real-time insights and analytics. Using GPUs, businesses can demonstrate competitive edge, unparalleled data-driven decision-making, and real-time improvement in products & services in line with changing market conditions and business circumstances.
GPU-accelerated Big Data systems are scalable and flexible, deliver unprecedented performance, cost-effectiveness and energy efficiency – in short, they are perfect for tech-savvy businesses in cut-throat domains which must instantaneously adapt to changing market conditions.
When discussing Big Data and gargantuan databases, a key application of GPU computing is database security. This has two-fold advantages – (a) organizations ensure that their databases are seamlessly encrypted and safe from hackers and other bad actors, and (b) GPU-supported systems can detect cyberattacks in real-time as they happen, authorizing organizations to respond immediately and prevent further damage from happening.
More and more researchers, data scientists and enterprises are relying on GPUs for Big Data processing. Handling bigger, more complicated datasets faster vis-a-vis GPU arrays allows the construction and modification of larger and more complex data models, including incorporation of additional variables and use cases. This, in turn, leads to improved performance outcomes, be they enhanced accuracy in image/ speech recognition, increased depth of scientific simulations (fluid dynamics, genetic/ protein modeling etc.) or speed in market prediction/ inference systems.
How to Choose the Right GPU for Big Data Processing?
Choosing the right hardware is key to extracting the best performance. When it comes to selecting a GPU for Big Data processing, there are a few essential things to remember.
First, you must figure out what sort of analytics operations the data must be subjected to. This can bring a major re-orientation in the GPU machines being deployed. For example, if the GPU is going to be used for ML operations, it needs to undertake higher quantum of parallel data processing for which more number of cores are always preferable. If the GPU is supposed to be used for sequential operations, fewer processing cores might suffice.
The next thing to consider is how much memory the GPU will need. Most professional engineering and visualization applications require massive data volumes to be stored in GPU memory while the workloads are being processed. Any shortfall in GPU memory won’t just affect productivity and creativity, but may also introduce information bottlenecks.
GPUs with more advanced versions of GDDR SDRAM (Graphics Double Data Rate Synchronous Dynamic RAM) are preferable since these prioritize processing bigger data volumes. Nvidia’s Ampere architecture utilizes GDDR6X memory, the latest in this class of GPU memory, in consumer-grade GPUs. The A100 Data center GPU boasts of eye-popping 80 GB HBM2e on-chip memory!
Note that HBM2e is the best choice for AI/ ML/ DL, 3D modelling and video editing/ simulation tasks, whereas GDDR6X memory reigns supreme as far as high-res gaming is concerned.
Lastly, it is essential to consider how much power the GPU and associated networking hardware consumes. Big Data processing can be heavily electricity-guzzling, especially when workloads must run continuously, for instance in customer-facing enterprises or applications relying on real-time data inputs. Deploying a more energy-conservative GPU is best both for the pocket and the environment. Shifting to a Cloud GPU service such as Ace Cloud Hosting can literally be a life-saver.
GPU computing is a critical technology that can empower businesses to extract more insights, better insights from the data available with them from multiple disparate sources. This, in turn, can enable them to sprint far ahead of their competitors in high-velocity industries.
Besides AI-accelerated automation and ML/DL-facilitated IoT, Big Data Analytics has emerged as a frontrunner across industries for improving performance, scalability, security, cost-effectiveness and energy efficiency.
While GPU computing has the potential to emerge as the panacea for all Big Data processing needs, the exorbitant cost of advanced GPUs continues to be a spoilsport. Thankfully, there is another option to explore – switching to Cloud GPUs which deliver the same high-benchmark performance at a fraction of the price. With Cloud GPUs, you can opt for a pay-on-the-go model based on your hardware requirements and actual usage.
This is a significant benefit for small businesses and frees up their financial resources for investing in innovation and R&D instead of expensive IT infrastructure. Moreover, Cloud GPU instances can be scaled up or down as well as customized seamlessly with necessary Compute/ RAM resources as per your business requirements.