Type-safe and portable support for packed data
April 28, 2025 Β· Declared Dead Β· π European Conference on Object-Oriented Programming
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Arthur Jamet, Michael Vollmer
arXiv ID
2504.20166
Category
cs.PL: Programming Languages
Citations
0
Venue
European Conference on Object-Oriented Programming
Last Checked
4 months ago
Abstract
When components of a system exchange data, they need to serialise the data so that it can be sent over the network. Then, the recipient has to deserialise the data in order to be able to process it. These steps take time and have an impact on the overall system's performance. A solution to this is to use packed data, which has a unified representation between the memory and the network, removing the need for any marshalling steps. Additionally, using this data representation can improve the program's performance thanks to the data locality enabled by the compact representation of the data in memory. Unfortunately, no mainstream programming languages support packed data, whether it's out-of-the-box or through a compiler extension. We present packed-data, a Haskell library that allows for type safe building and reading of packed data in a functional style. The library does not rely on compiler modifications, making it portable, and leverages meta-programming to allow programmers to pack their own data types effortlessly. We evaluate the usability and performance of the library, and conclude that it allows traversing packed data up to 60% faster than unpacked data. We also reflect on how to enhance the performance of library-based support for packed data. Our implementation approach is general and can easily be used with any programming languages that support higher-kinded types.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Programming Languages
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
R.I.P.
π»
Ghosted
Glow: Graph Lowering Compiler Techniques for Neural Networks
R.I.P.
π»
Ghosted
Learnable Programming: Blocks and Beyond
R.I.P.
π»
Ghosted
Scenic: A Language for Scenario Specification and Scene Generation
R.I.P.
π»
Ghosted
Vandal: A Scalable Security Analysis Framework for Smart Contracts
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted