A survey of machine learning for computer architecture and systems
It has been a long time that computer architecture and systems are optimized for efficient
execution of machine learning (ML) models. Now, it is time to reconsider the relationship …
execution of machine learning (ML) models. Now, it is time to reconsider the relationship …
Vectorization for digital signal processors via equality saturation
Applications targeting digital signal processors (DSPs) benefit from fast implementations of
small linear algebra kernels. While existing auto-vectorizing compilers are effective at …
small linear algebra kernels. While existing auto-vectorizing compilers are effective at …
VeGen: a vectorizer generator for SIMD and beyond
Vector instructions are ubiquitous in modern processors. Traditional compiler auto-
vectorization techniques have focused on targeting single instruction multiple data (SIMD) …
vectorization techniques have focused on targeting single instruction multiple data (SIMD) …
Difftune: Optimizing cpu simulator parameters with learned differentiable surrogates
CPU simulators are useful tools for modeling CPU execution behavior. However, they suffer
from inaccuracies due to the cost and complexity of setting their fine-grained parameters …
from inaccuracies due to the cost and complexity of setting their fine-grained parameters …
uiCA: Accurate throughput prediction of basic blocks on recent Intel microarchitectures
Performance models that statically predict the steady-state throughput of basic blocks on
particular microarchitectures, such as IACA, Ithemal, llvm-mca, OSACA, or CQA, can guide …
particular microarchitectures, such as IACA, Ithemal, llvm-mca, OSACA, or CQA, can guide …
Coyote: A compiler for vectorizing encrypted arithmetic circuits
R Malik, K Sheth, M Kulkarni - Proceedings of the 28th ACM International …, 2023 - dl.acm.org
Fully Homomorphic Encryption (FHE) is a scheme that allows a computational circuit to
operate on encrypted data and produce a result that, when decrypted, yields the result of the …
operate on encrypted data and produce a result that, when decrypted, yields the result of the …
Evaluation of compilers' capability of automatic vectorization based on source code analysis
JG Feng, YP He, QM Tao - Scientific Programming, 2021 - Wiley Online Library
Automatic vectorization is an important technique for compilers to improve the parallelism of
programs. With the widespread usage of SIMD (Single Instruction Multiple Data) extensions …
programs. With the widespread usage of SIMD (Single Instruction Multiple Data) extensions …
Compiler auto-vectorization with imitation learning
Modern microprocessors are equipped with single instruction multiple data (SIMD) or vector
instruction sets which allow compilers to exploit fine-grained data level parallelism. To …
instruction sets which allow compilers to exploit fine-grained data level parallelism. To …
A tensor compiler with automatic data packing for simple and efficient fully homomorphic encryption
Fully Homomorphic Encryption (FHE) enables computing on encrypted data, letting clients
securely offload computation to untrusted servers. While enticing, FHE has two key …
securely offload computation to untrusted servers. While enticing, FHE has two key …
Facile: Fast, accurate, and interpretable basic-block throughput prediction
Basic-block throughput models such as uiCA, IACA, GRANITE, Ithemal, llvm-mca, OSACA,
or CQA guide optimizing compilers and help performance engineers identify and eliminate …
or CQA guide optimizing compilers and help performance engineers identify and eliminate …