Last updated on January 6th, 2023
With the rise in population and the number of products we are purchasing, the transactions per minute have also increased drastically. Companies store this data and its petabytes of structured and unstructured data (that banks and other financial companies can use) to forecast client behavior and develop strategies.
Information that has been organized within a company to offer crucial decision-making insights is known as structured data. Unstructured data is available from numerous sources in growing quantities and presents important analytical potential.
To process and analyze this data most finance companies are not equipped with the right hardware and often struggle with processing such large data.
Outsourcing this data is also not always not feasible as it costs lot of time and does not keep up with daily operations of the company.
Fig 1: Increase in the number of transactions from 2015 to 2022 according to Statista
In advanced financial analytics, central processing units (CPUs) have long served as the main calculation engine. However, with large amounts data, the processing is usually slow and takes forever to complete.
But recently, financial companies have started utilizing graphics processing units (GPUs), which are cluster of Compute Unified Device Architecture (CUDA) cores made to carry out quick calculations and are much faster that the computing cores of a CPU.
But it is highly impossible for finance companies to provide GPU based machines for its employees and upgrade them regularly. Some companies have their own servers to process and analyze this data but these servers are humongous and occupy a lot of space.
Many finance companies are also not equipped with the IT teams who can manage these servers regularly which causes malfunctioning and shutting down.
To combat this problem, companies can easily rely on cloud providers for a GPU-based cloud. By choosing a cloud service, the responsibility to upgrade hardware and maintain the servers is defaulted to the provider and you can focus on the tasks at hand. Users can also select the GPU they want and can either upgrade or degrade them at will.
Scroll down to read more on how GPUs benefit financial analysis.
Why you might want to use a GPU alongside a CPU
GPUs are special type of processors that are designed to perform multiple tasks that are similar over a large scale. Here are a few examples where they are useful over a CPU.
Higher data throughput and parallel processing
A server GPU consists of thousands of cores that work concurrently on various pieces of data to complete one task. Because of this, a GPU can accelerate particular processes beyond the capabilities of a CPU by pushing enormous volumes of processed data through a workload.
Due to the large number of cores in a GPU, it can perform multiple tasks in parallel which allows it to process different parts of the data at once.
GPUs are ideally suited to assist analytics applications in processing big bases of data from many sources. These same GPUs can also handle the computing required for deep data sets linked to fields of study like the life sciences and medicine.
The GPU’s ability of neural networks, especially those employed for deep-learning algorithms, to handle huge volumes of training data through tiny nodes of operations underlies their functionality.
Unlike CPUs, multi number of GPUs can be connected in parallel to improve the systems graphics power, vRAM and work really well for tasks that involve large data sets.
Why are many financial analysts employing a GPU in their workloads
Financial analysts often require high performance computing engines to run their machine learning (ML) and data analysis operations. GPUs will help with tasks where repeated calculations are required.
Here are few use cases for GPU in the hands of a financial analyst.
Big Data and AI/ML
Every day, billions of dollars transact across worldwide marketplaces, and analysts are entrusted with swiftly tracking this data properly and securely in order to make predictions, discover patterns, and develop predictive strategies.
The techniques used to gather, analyze, store, and interpret this data have a big impact on how valuable it is. Due to the inability of legacy systems to handle unstructured and segmented data without laborious and costly IT involvement, analysts are increasingly selecting cloud data solutions.
To digest the essential data and provide reliable results that meet the expectations of the user, these processes require artificial intelligence. Many financial analysts prefer a GPU because CPU are not particularly good for AI/ML workloads.
Accelerated Risk Analysis
For financial analytics, delivering real-time risk analysis data has been a huge technical problem. To solve this, companies can employ GPUs in their computers which are much faster than CPUs due to the sheer amount of performance cores they have.
With the help of a powerful GPU, customers can run custom functions such as Monte Carlo simulations can run inside a database with an analytics API. These simulations can help determine how risky any project can be and more the number of simulations, the better the accuracy is.
Parallel Computing for Identifying Fraud
GPUs enable users to perform parallel computing which is a process where a complex calculation is broken down into simpler calculations. All these calculations are equally distributed among the GPU cores and are stitched up at the end of the process.
The complex and high-cardinality datasets that are commonly used to detect fraud present unique challenges because they are difficult to index.
Indexing becomes less crucial due to the sufficient brute force provided by GPUs, which significantly improves the efficiency of searching for anomalies in various streams of transaction and log data.
Sentiment Analysis in Real Time
To inform trading models, quantitative traders are using sentiment indicators from social media, supply chain research, news, corporate forums, and even satellite data. On these text-heavy and geo-temporal datasets, NLP and ML techniques can be used to find possibilities and improve alpha.
All these simulations run significantly fast on a GPU like A100 as it has nearly 7000 CUDA cores which all run in parallel to deliver the maximum possible whilst the CPU is just stuck at around 10 cores.
GPUs have since become the platform of choice to train ML and DL models for any analyst or a data scientist.
CPU are usually low power consuming processors and are idle for small and day-to-day tasks. But when it comes to financial analysis which require lot of computational power and should be able to run complex calculations and learning.
CPUs in general are limited by the amount of power they can draw due to its size, architecture and number of cores but all these issues are solved with a GPU which are large and can handle a lot more power than a CPU.
GPUs like A100 can draw a power of around 400W and some users even prefer to use a multi-GPU setup to further boost their performance.
Milliseconds matter in the trading of stocks. Computers go through enormous amounts of historical data and use mathematical models to compare historical trends to present pricing patterns, which is how decisions are made.
Trading firms can store all of their historical data into memory and process it in parallel using GPU in-memory databases. Because some GPU databases are ML-optimized, computers can find patterns in data that people wouldn’t notice.
Additionally, they are perfectly suited for handling streaming data. By combining these capabilities, traders may do calculations on real-time pricing information, enabling them to make decisions almost instantly and place more confident transactions.
Also Read: What is Public Cloud
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Ampere series GPUs are the most powerful line of Nvidia GPUs and you can also scale them up upto 4 GPUs. It is much easier to scale your GPUs with us rather than purchasing your own server and adding those GPUs yourself.
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