Tootfinder

from my link log —
Faster remainder by multiplication, with applications to compilers and software libraries.
https://arxiv.org/abs/1902.01961
saved 2021-05-23

Faster Remainder by Direct Computation: Applications to Compilers and Software Libraries
On common processors, integer multiplication is many times faster than integer division. Dividing a numerator n by a divisor d is mathematically equivalent to multiplication by the inverse of the divisor (n / d = n x 1/d). If the divisor is known in advance---or if repeated integer divisions will be performed with the same divisor---it can be beneficial to substitute a less costly multiplication for an expensive division. Currently, the remainder of the division by a constant is computed from…

The 81st edition of De Programmatica Ipsum is out!
This month, we worry about the perils of vibe coding in the minds of new generations of software developers; in the Library section, we review "Geek Sublime" by Vikram Chandra; and in our Vidéothèque section, we watch a 1986 interview of Grace Hopper at "Late Night with David Letterman".

Issue 081: Compilers
Welcome to the 81st issue of De Programmatica Ipsum, about Compilers. In this edition, we worry about the perils of vibe coding in the minds of new generations of software developers; in the Library section, we review "Geek Sublime" by Vikram Chandra; and in our Vidéothèque section, we watch a 1986 interview of Grace Hopper at "Late Night with David Letterman".

Identity Testing for Circuits with Exponentiation Gates
Jiatu Li, Mengdi Wu
https://arxiv.org/abs/2506.04529 https://arxiv.org/pdf/25…

Identity Testing for Circuits with Exponentiation Gates
Motivated by practical applications in the design of optimization compilers for neural networks, we initiated the study of identity testing problems for arithmetic circuits augmented with \emph{exponentiation gates} that compute the real function $x\mapsto e^x$. These circuits compute real functions of form $P(\vec x)/P'(\vec x)$, where both $P(\vec x)$ and $P'(\vec x)$ are exponential polynomials \[ \sum_{i=1}^k f_i(\vec x)\cdot \exp\left(\frac{g_i(\vec x)}{h_i(\vec x)}\right), \] for …

Pearl: Automatic Code Optimization Using Deep Reinforcement Learning
Djamel Rassem Lamouri, Iheb Nassim Aouadj, Smail Kourta, Riyadh Baghdadi
https://arxiv.org/abs/2506.01880

Pearl: Automatic Code Optimization Using Deep Reinforcement Learning
Compilers are crucial in optimizing programs and accelerating their execution. However, optimizing programs automatically using compilers is not trivial. Recent work has attempted to use reinforcement learning (RL) to solve this problem. It has limitations though. Current methods either do not support the optimization of general loop nests or can only be used to optimize loop nests seen during training. In this paper, we propose Pearl, a novel framework that uses deep reinforcement learning to …

Solsmith: Solidity Random Program Generator for Compiler Testing
Lantian Li, Zhihao Liu, Zhongxing Yu
https://arxiv.org/abs/2506.03909 https://

Solsmith: Solidity Random Program Generator for Compiler Testing
Smart contracts are computer programs that run on blockchain platforms, with Solidity being the most widely used language for their development. As blockchain technology advances, smart contracts have become increasingly important across various fields. In order for smart contracts to operate correctly, the correctness of the compiler is particularly crucial. Although some research efforts have been devoted to testing Solidity compilers, they primarily focus on testing methods and do not addres…

Large Processor Chip Model
Kaiyan Chang, Mingzhi Chen, Yunji Chen, Zhirong Chen, Dongrui Fan, Junfeng Gong, Nan Guo, Yinhe Han, Qinfen Hao, Shuo Hou, Xuan Huang, Pengwei Jin, Changxin Ke, Cangyuan Li, Guangli Li, Huawei Li, Kuan Li, Naipeng Li, Shengwen Liang, Cheng Liu, Hongwei Liu, Jiahua Liu, Junliang Lv, Jianan Mu, Jin Qin, Bin Sun, Chenxi Wang, Duo Wang, Mingjun Wang, Ying Wang, Chenggang Wu, Peiyang Wu, Teng Wu, Xiao Xiao, Mengyao Xie, Chenwei Xiong, Ruiyuan Xu, Mingyu Yan, Xiaoc…

Large Processor Chip Model
Computer System Architecture serves as a crucial bridge between software applications and the underlying hardware, encompassing components like compilers, CPUs, coprocessors, and RTL designs. Its development, from early mainframes to modern domain-specific architectures, has been driven by rising computational demands and advancements in semiconductor technology. However, traditional paradigms in computer system architecture design are confronting significant challenges, including a reliance on…

Improving compiler support for SIMD offload using Arm Streaming SVE
Mohamed Husain Noor Mohamed, Adarsh Patil, Latchesar Ionkov, Eric Van Hensbergen
https://arxiv.org/abs/2506.02233

Improving compiler support for SIMD offload using Arm Streaming SVE
The wider adoption of tightly coupled core-adjacent accelerators, such as Arm Scalable Matrix Extension (SME), hinges on lowering software programming complexity. In this paper, we focus on enabling the use of SME architecture in Streaming Scalable Vector Extension (SSVE) mode for workloads written in C/C++. While current compilers optimize loops for all types of SIMD instructions, these techniques primarily target vector units within the core and falter when applied to disaggregated, core-adja…

This https://arxiv.org/abs/2503.19449 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

VecTrans: Enhancing Compiler Auto-Vectorization through LLM-Assisted Code Transformations
Auto-vectorization is a fundamental optimization for modern compilers to exploit SIMD parallelism. However, state-of-the-art approaches still struggle to handle intricate code patterns, often requiring manual hints or domain-specific expertise. Large language models (LLMs), with their ability to capture intricate patterns, provide a promising solution, yet their effective application in compiler optimizations remains an open challenge due to issues such as hallucinations and a lack of domain-sp…

from my link log —
Representing type lattices compactly.
https://bernsteinbear.com/blog/lattice-bitset/
saved 2025-03-12 https://…

LEGO-Compiler: Enhancing Neural Compilation Through Translation Composability
Shuoming Zhang, Jiacheng Zhao, Chunwei Xia, Zheng Wang, Yunji Chen, Xiaobing Feng, Huimin Cui
https://arxiv.org/abs/2505.20356

LEGO-Compiler: Enhancing Neural Compilation Through Translation Composability
Large language models (LLMs) have the potential to revolutionize how we design and implement compilers and code translation tools. However, existing LLMs struggle to handle long and complex programs. We introduce LEGO-Compiler, a novel neural compilation system that leverages LLMs to translate high-level languages into assembly code. Our approach centers on three key innovations: LEGO translation, which decomposes the input program into manageable blocks; breaking down the complex compilation p…