If you’re a software developer, whether you’re writing embedded code for a smart thermostat, a high-frequency trading algorithm, or a mobile gaming engine, you’ve probably heard that “performance matters.” But what does “performance” actually mean in practice? Is it raw clock speed? How quickly a machine runs a single math operation? Or something far more nuanced that captures how real code behaves on real hardware? For nearly 50 years, a little test called the Whetstone benchmark has been the quiet backbone of that conversation—and as a supplier who builds and maintains Whetstone benchmark tools for engineers and product teams around the world, I’ve seen firsthand how it’s evolved to meet the messy, complex needs of modern development. Whetstone
Most benchmarks that crop up in tech have short, flashy lives. They’re designed to sell a new processor, make a social media post go viral, or hype up a new programming language. The Whetstone benchmark is different. It started in 1972, when a group of computer scientists at the Theoretical Physics Division of the UK’s Atomic Energy Research Establishment needed a way to measure how well their new CDC 7600 supercomputer was running the FORTRAN code they relied on for nuclear physics simulations. Back then, there was no standard way to gauge programming language performance. A computer might have a faster clock, but slow down when running loops with floating-point math. Another might brag about integer speed, but choke on subroutine calls. The team, led by Harold Curnow, took a set of routines pulled from real physics code and grouped them into a test suite. They named it after Whetstone, the Leicestershire town where their lab was located—a small, humble name for a tool that would become an industry standard.
For decades, Whetstone’s core purpose stayed the same: it’s not a synthetic, made-up test. It’s a distillation of the kind of code that developers actually write. The original suite included subroutines for matrix algebra, trigonometric functions, loops, and conditional branches—all the building blocks of scientific computing, which was the most demanding programming work of the 1970s and 1980s. Unlike the SPEC benchmarks that emerged later (which are often based on entire applications, like web servers or compilers), Whetstone’s small, focused structure lets it zero in on specific weaknesses in a compiler or processor. Back when I got started in this business 15 years ago, I’d get calls from teams of FORTRAN developers at NASA or Lockheed Martin, all asking the same question: “Is our compiler optimizing our code correctly, or is it gaming the big SPEC numbers?” Whetstone was their go-to answer. It didn’t care if a compiler could run a web browser fast. It cared if it could run the differential equations that simulate a satellite’s orbit accurately.
As computing shifted from supercomputers to desktops, then to mobile devices and embedded systems, Whetstone had to adapt—otherwise, it would have faded away. For a long time, it was often dismissed as a “legacy” benchmark, too focused on 1970s-style math to matter for modern code that runs on phones or cloud servers. But that’s where my work as a supplier comes in. Over the past decade, we’ve updated the Whetstone suite to fit the realities of 21st century development. We didn’t throw out its core purpose; we expanded it. Today’s Whetstone benchmark includes variants for C, Python, Rust, and even embedded C, so teams building everything from AI edge devices to industrial control systems can use it. We’ve added modular subroutines for string manipulation, memory access patterns common in data processing, and even small snippets of parallel code, to match the multi-core architectures that power everything from laptops to cloud clusters.
That adaptability is why Whetstone still has a seat at the table, even in an era of hyper-specific benchmarks. Let’s be honest: no single benchmark is perfect. A benchmark that measures a GPU for gaming is useless for a database server, and vice versa. But Whetstone fills a gap that almost no other test can. It’s the only general-purpose benchmark that’s stood the test of time because it’s rooted in real developer behavior. When a startup building a real-time health monitoring device wants to test how its embedded code handles floating-point calculations without the noise of a full medical application, it turns to Whetstone. When a compiler team wants to debug a new optimization that breaks complex conditional logic, they run Whetstone, not just the flashy new SPEC tests. When a university lab is studying how programming languages handle mathematical work, Whetstone is still the standard because it’s transparent, well-documented, and has 50 years of validation from thousands of research papers.
Of course, Whetstone isn’t just for big teams working on high-stakes science. In my experience, most of our customers are small to mid-sized development teams that don’t have huge QA budgets. They’re building tools, apps, and embedded systems where even a tiny performance gap can sink a product. Last year, we worked with a small team at a climate tech startup that was building a tool to simulate carbon capture systems. Their initial prototype ran on a powerful laptop, but when they tried to port it to a low-cost Raspberry Pi for field testing, it was 10 times slower than they needed. They used Whetstone to isolate the problem: their compiler was poorly optimizing the trigonometric subroutine that formed the core of their simulation, which the larger, faster benchmarks they’d been using didn’t pick up. Whetstone let them fix that one issue, and suddenly their port ran fast enough for field use. That’s the kind of impact Whetstone has every day—something the industry often overlooks when it’s talking about the next big AI chip or smartphone launch.
But here’s the part that people don’t often talk about: Whetstone isn’t just a test of hardware or code. It’s a test of how well a development ecosystem works together. When a team runs Whetstone, they’re not just getting a single number (the old “million Whetstone operations per second” metric, or MWIPS). They’re seeing how their compiler interacts with the processor’s floating-point unit, how memory access patterns affect performance, and how their code’s structure impacts speed. It’s a diagnostic tool, not a sales pitch. That’s why as a supplier, we focus on more than just providing a test suite. We work with customers to help them interpret the results, to integrate Whetstone into their continuous integration pipelines, and to understand how to use it to improve their code, not just compare one machine to another. Too many companies treat benchmarks as a checkbox: run a test, post the number, and move on. Whetstone’s real value is in the conversation it starts, not the final result.
Inevitably, there are criticisms of Whetstone, and it’s important to acknowledge them. Some say it’s too focused on floating-point math, which is less important for the integer-heavy code that powers web servers and machine learning models. Others argue that its small size means compilers can “cheat” by optimizing away parts of the code, leading to unrealistic numbers. Those critiques aren’t wrong—they’re why Whetstone isn’t the only benchmark anyone uses. But that’s exactly its strength. It’s a complement to other tools, not a replacement. For teams building embedded systems or scientific software, it’s irreplaceable. For teams building cloud services, it’s a useful way to test the numerical stability and optimization of their code, even if they pair it with other tests for throughput. The key is understanding when to use it, and how to interpret its results.
Over the next 50 years, Whetstone will keep evolving, too. As quantum computing, edge AI, and new programming paradigms change what “performance” means, we’ll be there updating the suite to match. But one thing won’t change: its core purpose of being a tool for developers, not a marketing gimmick. Too many benchmarks come and go, hyped up for a quarter then forgotten. Whetstone is still here because it was built by developers, for developers, to solve a problem that still matters today: how to write code that works well, reliably, and efficiently, on whatever hardware it’s running.
If you’re a developer, a product lead, or a QA team looking to integrate a reliable, long-standing benchmark into your workflow, or if you’re looking for a custom-tailored version of Whetstone to fit your specific use case, we’re here to help. We’ve worked with everyone from university research labs to global semiconductor companies, and we can tailor our Whetstone benchmark tools to your needs, whether you’re testing embedded code, compilers, or multi-core systems. To discuss your requirements and find a solution that fits your project, get in touch with our team to start the conversation.
Coated Abrasives References
- Curnow, H. J., & Wichmann, B. A. (1976). A synthetic benchmark. The Computer Journal, 19(1), 43-49.
- IEEE Std 1003.1-2017 (POSIX). (2017). Information Technology—Portable Operating System Interface (POSIX). Institute of Electrical and Electronics Engineers.
- Wichmann, B. A. (1995). The Whetstone benchmark: a history. Software: Practice and Experience, 25(1), 95-102.
- Hennessy, J. L., & Patterson, D. A. (2019). Computer Architecture: A Quantitative Approach (6th ed.). Morgan Kaufmann Publishers.
Zibo Longshine International Co., Ltd.
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