How To Accelerate Prediction & Forecasting With GPU?

Prediction and forecasting are critical in every aspect of life.

Students attempt to predict the weightage a particular topic might accrue in examinations. Businesses forecast customer demand in response to a variety of parameters including marketing campaigns.

Agriculturalists predict optimal harvesting timelines based on weather conditions. Governments forecast movements in currency and financial markets in response to global events and political upheavals.

You get the drift.

What Exactly are Prediction and Forecasting?

Prediction involves analysing data from historical trends, incorporating existing knowledge and experience, and applying it to the future to estimate how an event will turn out or an individual/ trend will perform at some point in time.

Forecasting, also known as Predictive Analytics, involves projecting an outcome based on factors such as past statistical trendlines, current challenges and opportunities, real-time market/ economic conditions, as well as external factors outside of direct human control such as weather phenomenon, research breakthroughs, etc.

Thus, Prediction is intuitive, probabilistic, seeking to understand future trends. Forecasting, on the other hand, is objective decision-making based on solid statistical data. Both terms are often (erroneously) used interchangeably. Both of them constitute critical business functions necessary for informed decision-making.

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Enterprises today hire batteries of highly trained analysts or depend on encyclopedic reports from prestigious analytics firms. Moreover, these firms are no longer limited to predicting market trends, business opportunities or advertising strategy returns, but have even branched out into recruitment strategies and HR spheres.

In short, even forecasting if a new hire will contribute to the organization’s bottom-line or not can be tooled based on historical trends, educational achievements, past performance, etc.

Read: Accelerate Your Data Science With Nvidia GPUs

Challenges of Prediction and Forecasting

Timeline accuracy

Shorter forecasting periods naturally provide significantly accurate data compared to longer forecasting periods.

It is easier to predict that the earth will continue rotating on its axis over the next decade as it has done for millions of years. On the other hand, it is next to impossible to accurately pinpoint when earth’s rotation will change its direction, even after considering comprehensive historical data and detailed statistical models.

Similarly, forecasting where your business will be in the next two years based on past two years’ data is more realistic vis-a-vis forecast for the next ten years based on previous ten years’ trends.

The decadal variations are simply too substantial in such day-to-day use cases. They are far better suited for geological or weather-related or planetary considerations. And even there, more the variations, less accurate the forecast.

Input Data Accuracy

Every prediction is completely dependent on input data and previous experience.

If the historical data is inaccurate or full of inconsistencies/ human errors, or if the input data does not take into consideration all the necessary variables, the forecast generated will be unreliable for business needs.

Difficulties in Statistical Modelling

Building a comprehensive and highly accurate predictive model that takes into consideration all the business-specific variables remain a near-insurmountable challenge for most enterprises.

You may want to predict whether a potential customer will make a purchase based on past shopping behaviour, browsing history, demographic profile, economic profile, product quality, price factors, marketing/ discount campaign viability, etc. There exist multiple ways to build this kind of model, and each can predict a different outcome!

Of course, no statistical model can be ascertained to be 100% accurate. Outliers exist, and so do Spiders Georg memes.

CPU vs. GPU for Prediction and Forecasting

Prediction and forecasting applications necessitate massive data processing and therefore require substantial computing resources. Inefficient systems unable to handle such massive workloads might not only introduce numerous information bottlenecks, but also eventually cause the statistical model to fail when variables are introduced/ modified.

This is a universal problem for companies involved in Big Data processing and Data Analytics. Thus, the incorporation of GPU computing which can minimize the cycle time taken for complex matrix-based calculations compared to standard CPU-based implementation.

An optimal system deploys both CPUs and GPU(s), keeping CPUs for standard computation and system management purposes while offloading the burden of data workload processing to the more advanced GPU(s).

How does a GPU Accelerate Prediction and Forecasting Tasks?

To understand this, we first need to understand the principles of GPU processing.

Specifically developed to expedite graphics rendering, the GPU works on images, calculating the colors, contours and shading of each pixel independent from other pixels in the image. Handling each pixel is isolation from other pixels has been determined to be more computationally-efficient compared to processing large chunks of pixels together.

Secondly, GPUs have hundreds or even thousands of cores to perform the same computation operation across different data sets (or pixels). These cores can run parallelly, processing multiple tasks simultaneously.

Advanced GPU cores are also endowed with predication capabilities and the ability to breakdown complicated workloads into smaller sub-tasks for efficient parallel processing. This is known as Single Instruction, Multiple Data (SIMD) architecture.

Thirdly, GPU RAM is dedicated memory and promises much higher clock speed than CPU memory. GPU memory can deliver several hundred GB data per second to the GPU, thereby enabling a GPU-equipped system to undertake data processing operations considerably faster than CPU-only systems.

Lastly, GPU cores also come equipped with on-chip register memory which is very fast, albeit available only in miniscule blocks to each core. Registers are used for storing operands for any operation being executed by the core. Availability of register memory considerably accelerates parallel processing manifolds.

In prediction and forecasting applications, just like processing individual pixels in graphics, GPU decomposes the data into smaller fragments and runs analytics algorithms on these easy-to-process fragments.

Moreover, these small datasets are processed simultaneously in parallel for higher throughput, thus achieving very high data processing efficiency. Note: The structure and nature of the overall data is not affected in any way by its fragmentation.

Read: CUDA Cores Vs. Tensor Cores: Which One is Right for Machine Learning?

Selecting a GPU for Prediction/ Forecasting Operations

GPUs are excellent for Data Analytics, Big Data processing, development and training of Artificial Intelligence/ Machine Learning models, and pattern recognition/ anomaly detection. Prediction and forecasting operations rely on all of these applications in one form or another.

Choosing the best GPU for prediction and forecasting must take several factors into consideration depending on the nature and complexity of the prediction/ forecasting model –

1. System memory utilization
2. GPU on-chip memory availability
3. Dataset size and complexity
4. Throughput optimization
5. Latency minimization

Metrics for Evaluating GPU Acceleration

How would you ensure that your GPU is appropriately utilized and provides optimal return on your investment? GPUs are becoming exorbitant with each generation, and there are the added costs of skilled manpower, networking and cooling resources, operations and maintenance.

Due to ineffective setup, coding inconsistencies and resource allocation challenges, projects often use only a small fragment of the available GPU resources. Prediction and forecasting operations crunch colossal datasets but need precise results in a timely manner for it to be worthwhile from a business perspective.

As the available computation resources are optimized and used in the right place at the right time, the forecasting model won’t require exaggerated timelines for processing the available data, refactoring the algorithms to incorporate new variables, or redesigning the underlying AI/ ML forecasting module.

To ensure efficient usage of the GPU, you should monitor the following metrics –

1. GPU Utilization – This metric measures the time your GPU kernels were running. This helps ascertain GPU capacity requirements and bottlenecks across different workloads. Following proper in-depth GPU utilization analysis, you can remediate bottlenecks and divert excess GPU resources towards the more resource-intensive predictive analytics workloads.
2. GPU Memory Access and Usage – It is the measurement of time for which the GPU’s memory controller was under use. Moreover, this consists of read-and-write operations that can help you efficiently batch your data.
3. Power Usage and Temperatures – Monitoring power usage and ensuring there is adequate power supply for temperature control is important when working with GPUs since excessive temperatures can result in thermal throttling and considerable efficiency drop.
4. Time-to-Solution – This is a measure of the training time for a prediction model at a pre-specified accuracy level. Depending on the GPU specifications, you can set batch sizes and accuracy for better time-to-solution metric.

Benefits of GPU-accelerated Predictions and Forecasting

(A)Benefits for Data Scientists

1. Reduced Wait Time – With GPU-accelerated parallel processing of data, the overall waiting time is significantly reduced, and large volumes of data are processed at super-high speeds. You can therefore focus on solving greater quantum of problems rather than waiting for previous processes/ calculations to complete.
2. Accurate Results – Data Scientists completely rely on the quality of the data available to them for deriving business-relevant insights. GPUs help sieve and query massive datasets at unbelievable speed, thereby leveraging the entirety of available data for predictive analytics. This in turn contributes to informed decision-making, better understanding of customer demands, enhanced accuracy, and improved targeted marketing.
3. No Refactoring – Intelligent tools allow you to build and experiment with data models and analytics without lengthy learning curve, nor do they require significant code changes in the already developed model when introducing new variables and perspectives. Reducing the requirement for constructing/ coding the model from scratch saves Data Scientists considerable headache and enables them to dedicate more time to experimenting with the predictive models.
4. Cost-Efficiency – The infrastructure cost for Data Scientists involved in predictions and forecasting projects can be appreciably reduced while simultaneously improving efficiency by deploying GPU-accelerated solutions rather than CPU-based systems.

(B)Benefits for IT Infrastructure

1. Better Return on Investment – GPUs contribute to improved efficiency, significant reduction in processing time, and better utilization of power and networking resources. Proper configuration of GPU systems thus triggers considerable reduction in infrastructure costs and a concomitant increase in the ROI.
2. Seamless Scaling – While scaling can be cumbersome, time-consuming and exorbitant for CPU and on-prem GPU systems, it ceases to be an issue once Data Scientists get a taste for Cloud GPU solutions. Cloud GPU lets users pay for the resources they utilize and scale up/down seamlessly as and when required at the click of a button. A sudden surge in input data and/or variables involved, scale up instantaneously with Cloud GPUs. A pressing need for refactoring, code changes or any other challenge with analytics/ forecasting algorithms, scale down for as long as you need. As simple as that!

Conclusion

Prediction and forecasting are computationally very intensive. For a long time, prediction and forecasting training models were limited by the hardware resources at hand. Days or even months would pass by in a daze even when training small models.

The introduction of GPUs and the parallel processing capabilities they deliver has completely revolutionized the field. Highly efficient and lightning-fast training of prediction/ forecasting models over notably larger databases is now becoming commonplace.

Better market insights through GPU-accelerated analytics is a gamechanger. Ace Cloud Hosting is a reputed Cloud GPU Provider offering state-of-the-art Nvidia GPUs that can propel your organization to the forefront of your industry through the riveting magic of prediction and forecasting. Get in touch with our technology geeks and discuss how we can assist in developing your prediction and forecasting systems.

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