Compiling a Higher-Order Smart Contract Language to LLVM
August 12, 2020 ยท Declared Dead ยท ๐ arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Vaivaswatha Nagaraj, Jacob Johannsen, Anton Trunov, George Pรฎrlea, Amrit Kumar, Ilya Sergey
arXiv ID
2008.05555
Category
cs.PL: Programming Languages
Citations
2
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Scilla is a higher-order polymorphic typed intermediate level language for implementing smart contracts. In this talk, we describe a Scilla compiler targeting LLVM, with a focus on mapping Scilla types, values, and its functional language constructs to LLVM-IR. The compiled LLVM-IR, when executed with LLVM's JIT framework, achieves a speedup of about 10x over the reference interpreter on a typical Scilla contract. This reduced latency is crucial in the setting of blockchains, where smart contracts are executed as parts of transactions, to achieve peak transactions processed per second. Experiments on the Ackermann function achieved a speedup of more than 45x. This talk abstract is aimed at both programming language researchers looking to implement an LLVM based compiler for their functional language, as well as at LLVM practitioners.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Programming Languages
R.I.P.
๐ป
Ghosted
R.I.P.
๐ป
Ghosted
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
R.I.P.
๐ป
Ghosted
Glow: Graph Lowering Compiler Techniques for Neural Networks
R.I.P.
๐ป
Ghosted
Learnable Programming: Blocks and Beyond
R.I.P.
๐ป
Ghosted
Scenic: A Language for Scenario Specification and Scene Generation
R.I.P.
๐ป
Ghosted
Vandal: A Scalable Security Analysis Framework for Smart Contracts
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
๐ป
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
๐ป
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
๐ป
Ghosted