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 …

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 …

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 …

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 …

Search-based tensor compilers can greatly accelerate the execution of machine learning
models by generating high-performance tensor programs, such as matrix multiplications and …

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 …

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 …

A major challenge for many database management tasks including admission control, query
scheduling, progress monitoring and self‐driving data storage systems is to enhance …

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 …

Growing heterogeneity and configurability in HPC architectures has made auto-tuning
applications and runtime parameters on these systems very complex. Users are presented …