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ARM-Based AWS Lambda with Graviton2: What Developers Need to Know

October 5, 2021

IoT

Cost Optimization

Two technical edge cases when migrating AWS Lambda to ARM/Graviton2: floating-point comparison precision and undefined C behavior with negative float casting. Learn migration considerations.

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This blog was originally written and published by Trek10, which is now part of Caylent.

The announcement of Graviton2 support for AWS Lambda, which boasts up to 19 percent better performance at 20 percent lower cost compared to its x86-based predecessor, has left many of us wondering “great, so what’s the catch?”

For the vast majority of developers, there is no catch—almost everything should “just work.” In this brief post though, we will walk you through a few possible issues you could encounter and how to deal with them.

A typical use-case for AWS Lambda is running a relatively simple procedure written in a high-level programming language, with perhaps a few external packages installed, that finishes its execution in milliseconds. Everything within the standard library of a high-level programming language, such as Python, should work out of the box. Additionally, nearly all commonly used packages within these ecosystems have already been ported to arm architectures.

The only circumstances in which you, as a developer, might (emphasis on the might) run into an issue with Graviton-based AWS Lambda include floating point comparisons and converting floats, with relation to these operations being performed within portions of imported packages that make calls to native C/C++ code.

The first potential issue, floating point comparisons, should be of little consequence. In short, float values determined within one architecture may slightly differ with the values determined by another. Mathematically speaking, the two values might be equivalent to one another, but when comparing them programmatically, i.e. float_from_x86 == float_from_arm, you may get “false” as a result. Again, for most use cases this limitation is completely irrelevant, but if you are putting incredibly precise workloads on AWS Lambda, it might be a good idea to review which numerical datatypes you are using and ensure appropriate levels of rounding are occurring.

The second issue, in regards to converting floats, pertains to undefined C behavior: