arXiv subjects

Krut Patel

Publications and source records attributed to Krut Patel.

2 recordsLinked to original sources

arXiv2026

First-Principles Turbulence-Driven Deflagration-to-Detonation Transition Mechanism for Near-Chandrasekhar Mass White Dwarf Progenitors

Type Ia supernovae (SNe Ia) play an important role throughout astrophysics, most notably as standardizable cosmological candles. Yet, their stellar progenitors and explosion mechanism remain areas of active investigation. For decades, the canonical model for normal brightness SNe Ia used in cosmology was a carbon-oxygen white dwarf (WD) accreting from a non-degenerate stellar companion, approaching the Chandrasekhar mass (M_Ch). Previously, all models of near-M_Ch SNe Ia invoked an ad hoc assumption on the critical process of detonation initiation, and could therefore be tuned to a variety of outcomes. Here, we present global 3D hydrodynamical simulations of near-M_Ch progenitors, which incorporate, for the first time, a laboratory-validated ab initio mechanism for the turbulence-driven deflagration-to-detonation transition (tDDT). The tDDT detonation mechanism is highly efficient, leading to detonation initiation which is prompt in comparison to most prior work. Despite spanning a factor of six in central ignition density and qualitatively distinct ignition topologies, all models converge on nearly identical synthetic spectra at peak luminosity, spectroscopically matched to the overluminous SN 1999aa. The turbulence-driven Chapman-Jouguet criterion drives each progenitor to a common detonation configuration from diverse initial conditions, providing a physical foundation for the ignition-insensitive detonation outcomes implicit in the empirical standardizability of SNe Ia. This provides the first physically motivated, self-consistent pathway for delayed detonation in SNe Ia simulations. Further work is necessary to understand how this mechanism might produce more delayed detonation initiation and potentially fail, thereby yielding SNe Iax.

astro-ph.HE↗

arXiv2025

Automatically Generating ML Compiler Backends from Tensor Accelerator ISA Descriptions

Machine learning (ML) compilers play a key role in enabling high-performance implementations of ML workloads. These compilers use existing CPU and GPU backends to generate device-specific code. In recent years, many tensor accelerators (or AI accelerators) have been designed to further accelerate these workloads, with commercial products like AWS Trainium publicly available. However, compared to commodity hardware, a majority of tensor accelerators do not have mature ML compiler backends with robust code generation support. Moreover, tensor accelerator designs are subject to fast iteration cycles, making it difficult to manually develop and maintain ML compiler backends. Therefore, to enable faster integration of novel tensor accelerator designs in ML infrastructure, we need to make the compiler backend construction process more agile. We introduce ACT, a compiler backend generator that automatically generates compiler backends for tensor accelerators, given just the instruction set architecture (ISA) descriptions. These backends are integrated with XLA, a production ML compiler. ACT uses a novel ISA-parameterized compilation algorithm to generate a compiler backend with an equality-saturation-based instruction selection phase and a constraint-programming-based memory allocation phase. We generated compiler backends for 6 accelerator platforms from industry (e.g., AWS Trainium, Intel AMX) and academia (e.g., Gemmini). We showed that these generated backends match or outperform commercial compiler backends and expert-written kernel libraries, while maintaining low compilation overheads. Notably, ACT-generated backend for AWS NKI ISA improved the code generation coverage for AWS Trainium by 2.3x compared with AWS's production compiler, neuronx-cc. ACT is part of a larger open-source ecosystem (https://github.com/act-compiler/act) built around our ISA description language, TAIDL.

cs.PL↗