A survey on compiler autotuning using machine learning
Since the mid-1990s, researchers have been trying to use machine-learning-based
approaches to solve a number of different compiler optimization problems. These …
approaches to solve a number of different compiler optimization problems. These …
Ithemal: Accurate, portable and fast basic block throughput estimation using deep neural networks
Predicting the number of clock cycles a processor takes to execute a block of assembly
instructions in steady state (the throughput) is important for both compiler designers and …
instructions in steady state (the throughput) is important for both compiler designers and …
Self-optimizing memory controllers: A reinforcement learning approach
Efficiently utilizing off-chip DRAM bandwidth is a critical issuein designing cost-effective,
high-performance chip multiprocessors (CMPs). Conventional memory controllers deliver …
high-performance chip multiprocessors (CMPs). Conventional memory controllers deliver …
Milepost gcc: Machine learning enabled self-tuning compiler
Tuning compiler optimizations for rapidly evolving hardware makes porting and extending
an optimizing compiler for each new platform extremely challenging. Iterative optimization is …
an optimizing compiler for each new platform extremely challenging. Iterative optimization is …
Predicting unroll factors using supervised classification
M Stephenson, S Amarasinghe - International symposium on …, 2005 - ieeexplore.ieee.org
Compilers base many critical decisions on abstracted architectural models. While recent
research has shown that modeling is effective for some compiler problems, building …
research has shown that modeling is effective for some compiler problems, building …
Automatic feature generation for machine learning--based optimising compilation
Recent work has shown that machine learning can automate and in some cases outperform
handcrafted compiler optimisations. Central to such an approach is that machine learning …
handcrafted compiler optimisations. Central to such an approach is that machine learning …
[图书][B] The compiler design handbook: optimizations and machine code generation
YN Srikant, P Shankar - 2002 - taylorfrancis.com
The widespread use of object-oriented languages and Internet security concerns are just the
beginning. Add embedded systems, multiple memory banks, highly pipelined units …
beginning. Add embedded systems, multiple memory banks, highly pipelined units …
GRANITE: A graph neural network model for basic block throughput estimation
Analytical hardware performance models yield swift estimation of desired hardware
performance metrics. However, developing these analytical models for modern processors …
performance metrics. However, developing these analytical models for modern processors …
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 …
Rollout strategies for sequential fault diagnosis
F Tu, KR Pattipati - IEEE Transactions on Systems, Man, and …, 2003 - ieeexplore.ieee.org
Test sequencing is a binary identification problem wherein one needs to develop a minimal
expected cost testing procedure to determine which one of a finite number of possible failure …
expected cost testing procedure to determine which one of a finite number of possible failure …