As supply chains generate ever-larger datasets and demand faster decisions, traditional central processing unit (CPU)-based systems are approaching their limits. To meet real-time requirements at scale, developers turn to accelerated computing powered by graphics processing units (GPUs). These massive parallel processors reshape how data is accessed, analyzed, and operationalized across the enterprise supply chain.
One expert at the forefront of this transformation is Meher Siddhartha Errabolu. Currently a technical architect at Blue Yonder, a world leader in supply chain management solutions. Siddhartha has over 20 years of experience in enterprise application development, from building microservices that handle millions of daily transactions to designing extract, transform, and load (ETL) frameworks using functional programming principles. Today, he leads GPU-based initiatives in real-time supply chain computing, helping organizations move beyond batch-based analytics into truly responsive systems.
Q: Why are GPUs growing in enterprise supply chain applications, and how do they differ from traditional CPUs?
Errabolu: Supply chain systems are increasingly data-intensive. Large retailers manage millions of stock keeping units (SKUs) across thousands of locations, generating hundreds of billions of data points when you factor in time-series data, forecast metrics, and historical inventory states. The latest CPUs, 24cores, handle instructions sequentially. That design works well for general-purpose tasks, but doesn’t scale well when instant decisions are needed across large datasets.
GPUs, in contrast, come with up to 21,760 cores (the latest Nvidia GeForce RTX 50 Series), enabling thousands of simultaneous operations. This parallelism makes GPUs ideal for environments where multiple calculations must be performed quickly and concurrently. In our work at Blue Yonder, we are successfully testing systems GPU acceleration to process 364 billion records in near real-time—something that would be unworkable using CPUs alone. While GPU efficiency gains are not always linear, we routinely observe 100 times performance improvements over traditional systems.
Q: How are supply chain workloads adapted to leverage GPU parallelism?
Errabolu: The key is to structure workloads using two complementary approaches: task-level and data-level parallelism. With task-level parallelism, multiple functions run on the same data. For example, users might simultaneously calculate inventory accuracy and storage utilization rate for a single product. Data-level parallelism applies the same function across multiple datasets, for example, running the same metric calculation across 10,000 SKUs at once.
To put this in perspective, one use case involved one million products spread across 3,500 stores, with two years of weekly data. That alone resulted in over 364 billion records, not including derived metrics. By applying parallelism, we reduced what would typically require hours into sub-second computations. This sets the stage for real-time responsiveness, which is becoming a baseline expectation in many enterprise settings.
Q: Where do traditional big data platforms fall short, and how does GPU architecture address that gap?
Errabolu: Big data platforms are often optimized for batch analytics. Systems extract, process, and review data overnight. While that model works for long-term planning, it doesn’t support real-time decisions. In contrast, GPU-backed microservices can return results in less than 50 milliseconds.
Imagine a retail user who wants to test how changing a shirt color from maroon to red might influence holiday sales. That analysis needs to run instantly and not as part of tomorrow’s report. This mirrors latency-sensitive domains like finance, where minor time delays translate into lost opportunities. GPU microservices bring the same low-latency paradigm to supply chains, allowing users to simulate and adjust operations in real time.
This agility also supports broader transformations. For example, companies implementing digital twins now expect their analytics layer to update continuously, not periodically. GPUs make this possible by reducing both compute time and response lag.
Q: How are in-memory databases used in GPU-based architectures, and what are the trade-offs?
Errabolu: In-memory databases reduce latency by storing data in random access memory (RAM) rather than on disk. Traditional options like Redis or H2DB improve access speed but are still CPU-driven. GPU-accelerated in-memory databases like MapD (now OmniSci) take it further. They combine fast data access with GPU-based parallel execution, accelerating transformation and retrieval.
These systems consume substantial memory. It’s not possible to keep all enterprise data in GPU memory, so it’s critical to carefully orchestrate what stays on disk, what loads into CPU RAM, and what gets promoted to GPU memory.
To manage this effectively, companies can use architecture that emphasizes selective data residency, with a focus on high-frequency access datasets, memory-aware processing, assigning tasks where block and thread models can be exploited, and tiered storage strategies, which use a hybrid of disk, RAM, and GPU memory. Organizations can also employ efficient resource profiling to prevent bloat and maintain responsiveness. It’s a balancing act. When done right, it delivers remarkable throughput improvements.
Q: What technical stack or programming models are enabling this transformation?
Errabolu: The shift to GPU-native computing often requires moving away from legacy tools. Java remains widely used but doesn’t map well to GPU programming. Compute Unified Device Architecture (CUDA), NVIDIA’s platform for GPU development, is based on C++. That’s where a lot of GPU optimization happens—defining thread groups, memory blocks, and execution flows.
In CUDA, users define how many blocks and threads are assigned to a task. For example, eight blocks with four threads each allow 32 parallel operations. Blocks share memory, which makes them ideal for task-level parallelism on shared data. These patterns enable users to compute derived measures, like inventory ratios or warehouse utilization, on thousands of products in parallel. This has also reignited interest in C++ across engineering teams.
Q: What is the future of accelerated computing in supply chain applications?
Errabolu: GPU-based computing is not just a trend. It’s fast becoming foundational for enterprise-scale analytics. Interest in streaming processors is also on the rise and brings even more flexibility to real-time dataflows. Quantum computing has promise but is very far away from commercialization, and currently quantum computing is 100,000 times more expensive an hour than classic computing.
In the near term, success hinges on how well organizations can tune their systems to maximize existing GPU hardware. That means designing accelerator-aware architectures where concurrency is built into the system design and memory tiers are explicitly managed.
Engineers are also learning from other domains. Scientific computing, for example, has long embraced GPU acceleration to manage large, simulation-heavy datasets. Supply chain data has a similar profile. The difference now is that enterprises expect sub-second responsiveness at scale and across distributed environments. For organizations that embrace this early, GPUs offer more than just speed. They unlock a new class of operational agility.
Q: How can engineers prepare to adopt GPU-based systems?
Errabolu: Three points stand out. First, move away from CPU-bound thinking and adopt concurrency as a design principle. Second, manage memory tiers deliberately—it’s vital for storage, CPU RAM, and GPU memory to work in harmony. Third, invest in developer skillsets that include C++, CUDA, and functional programming models. Those who embrace this shift early will be better equipped for real-time, data-intensive decision environments.
The Future of Computing
Accelerated computing opens new possibilities for supply chain systems, particularly in scenarios demanding real-time responsiveness and large-scale data processing. Organizations can reframe how they approach performance, scalability, and data access by shifting from CPU-bound architectures to GPU-driven models. As implementation efforts mature and development practices adapt primarily through tools like CUDA and memory-optimized architectures, the supply chain domain stands to benefit from the same computational breakthroughs that have transformed other high-performance fields.
About the Author
Paul Chaney is a seasoned writer, editor, and content strategist who helps businesses craft compelling, ethical marketing narratives through his consultancy, Prescriptive Writing. With a focus on clarity, authenticity, and responsible communication, Paul empowers organizations to tell their stories with purpose and precision. Connect with him on LinkedIn.
Disclaimer: The author is completely responsible for the content of this article. The opinions expressed are their own and do not represent IEEE’s position nor that of the Computer Society nor its Leadership.
