Abstract

Recently, there has been a lot of excitement around ML-enhanced (or learned) algorithms and data structures. For example, there has been work on applying machine learning to improve query optimization, indexing, storage layouts, scheduling, log-structured merge trees, sorting, compression, sketches, among many other data management tasks. Arguably, the ideas behind these techniques are similar: machine learning is used to model the data and/or workload in order to derive a more efficient algorithm or data structure. Ultimately, what these techniques will allow us to build are “instance-optimized” systems; systems that self-adjust to a given workload and data distribution to provide unprecedented performance and avoid the need for tuning by an administrator.

In this talk, I will first provide an overview of the opportunities and limitations of current ML-enhanced algorithms and data structures, present initial results of SageDB, a first instance-optimized system we are building as part of DSAIL@CSAIL at MIT, and finally outline remaining challenges and future directions.

Abstract

In this talk, I will tell 3 stories in learning patterns from ML in search and recommendations, to learned index structures and learned compilation.

Abstract

Compilers are the workhorse that bridge the gap between human readable and machine executable code. The diversity of modern programs, along with the advent of new and complex hardware architectures, has strained the capabilities of current compilers, making development and maintenance of automatic program optimizations in compilers exceedingly challenging. In spite of this, modern compiler optimizations are still constructed manually, expending considerable human effort.

In this talk, I will show how to construct compiler optimizations in a more automated manner using compiler auto-vectorization as an example. More specifically, I will show how to construct the three main components of a compiler optimization pass, namely, the transformation space, cost model and the optimization algorithm using both formal reasoning and machine learning.

First, I will demonstrate how to automatically generate a richer transformation space for compiler auto-vectorization using just the formal ISA semantics of a given hardware platform with the vectorizer generator Vegen. Vegen automatically supports newer vector instructions compared to manually-written vectorizers and outperforms production compilers on a number of important kernels.

Second, I will show how to automatically learn the optimization algorithm that selects the best transformation choice among valid vectorization opportunities exposed through the transformation space in Vemal. Vemal formulates the optimization problem as a sequential decision making process and shows that it is feasible to learn how to imitate a costly oracle.

Third, I will briefly present how to use data-driven techniques to learn compiler cost models with Ithemal. Ithemal automatically learns how to predict the throughput of basic blocks represented in a given ISA and the results show that it is significantly more accurate than state-of-the-art hand-written cost models used in production compilers.

The synergy in design of these three components will pave the way towards developing more automated means of constructing compiler optimizations that achieve state-of-the-art results.

Abstract

Cloud services are increasingly adopting new programming models, such as microservices and serverless compute. While these frameworks offer several advantages, such as better modularity, ease of maintenance and deployment, they also introduce new hardware and software challenges.

In this talk, I will briefly discuss the challenges that these new cloud models introduce in hardware and software, and present some of our work on accelerating critical microservice computation, and on employing ML to improve the cloud’s performance predictability and resource efficiency. I will first discuss Seer, a performance debugging system that identifies root causes of unpredictable performance in multi-tier interactive microservices, and Sage, which improves on Seer by taking a completely unsupervised learning approach to data-driven performance debugging, making it both practical and scalable.