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 …
Learning performance-improving code edits
With the waning of Moore's law, optimizing program performance has become a major focus
of software research. However, high-level optimizations such as API and algorithm changes …
of software research. However, high-level optimizations such as API and algorithm changes …
Programl: A graph-based program representation for data flow analysis and compiler optimizations
Abstract Machine learning (ML) is increasingly seen as a viable approach for building
compiler optimization heuristics, but many ML methods cannot replicate even the simplest of …
compiler optimization heuristics, but many ML methods cannot replicate even the simplest of …
IR2VEC LLVM IR Based Scalable Program Embeddings
We propose IR2Vec, a Concise and Scalable encoding infrastructure to represent programs
as a distributed embedding in continuous space. This distributed embedding is obtained by …
as a distributed embedding in continuous space. This distributed embedding is obtained by …
Tenset: A large-scale program performance dataset for learned tensor compilers
Search-based tensor compilers can greatly accelerate the execution of machine learning
models by generating high-performance tensor programs, such as matrix multiplications and …
models by generating high-performance tensor programs, such as matrix multiplications and …
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 …
Efficient Listing with Set Intersection Speedup
Listing all k-cliques is a fundamental problem in graph mining, with applications in finance,
biology, and social network analysis. However, owing to the exponential growth of the …
biology, and social network analysis. However, owing to the exponential growth of the …
Deepcm: Deep neural networks to improve accuracy prediction of database cost models
A major challenge for many database management tasks including admission control, query
scheduling, progress monitoring and self‐driving data storage systems is to enhance …
scheduling, progress monitoring and self‐driving data storage systems is to enhance …
Core placement optimization for multi-chip many-core neural network systems with reinforcement learning
Multi-chip many-core neural network systems are capable of providing high parallelism
benefited from decentralized execution, and they can be scaled to very large systems with …
benefited from decentralized execution, and they can be scaled to very large systems with …
Performance optimization using multimodal modeling and heterogeneous gnn
Growing heterogeneity and configurability in HPC architectures has made auto-tuning
applications and runtime parameters on these systems very complex. Users are presented …
applications and runtime parameters on these systems very complex. Users are presented …