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This article investigates block-level interference exploitation (IE) precoding for multiuser multiple-input-single-output (MU-MISO) downlink systems. To overcome the need for symbol-level IE precoding to frequently update the precoding matrix, we propose to jointly optimize all the precoders or transmit signals within a transmission block. The resultant precoders only need to be updated once per block, and while not necessarily constant over all the symbol slots, we refer to the technique as block-level slot-variant IE precoding. Through a careful examination of the optimal structure and the explicit duality inherent in block-level power minimization (PM) and signal-to-interference-plus-noise ratio (SINR) balancing (SB) problems, we discover that the joint optimization can be decomposed into subproblems with smaller variable sizes. As a step further, we propose block-level slot-invariant IE precoding by adding a structural constraint on the slot-variant IE precoding to maintain a constant precoder throughout the block. A novel linear precoder for IE is further presented, and we prove that the proposed slot-variant and slot-invariant IE precoding share an identical solution when the number of symbol slots does not exceed the number of users. Numerical simulations demonstrate that the proposed precoders achieve a significant complexity reduction compared against benchmark schemes, without sacrificing performance. 

Beyond diagonal reconfigurable intelligent surfaces (BD-RIS) with interconnected reflecting elements present an emerging technology for manipulating the propagation environment, and their new structure requires careful investigation. In this paper, we explore BD-RIS-aided power minimization beamforming, where the BD-RIS scattering matrix and transmit beamforming are jointly optimized under nonconvex constraints related to signal-to-interference-plus-noise ratio (SINR) thresholds and the structure of the scattering matrix. To efficiently solve the problem, we propose a single-loop algorithm, where we adopt a variable splitting strategy with an auxiliary variable to split the scattering matrix, and then alternately update the resulting variables. Through further derivations, we show that each nonconvex subproblem can be solved efficiently. Simulation results demonstrate the high efficiency of our proposed single loop algorithm and the effectiveness of BD-RIS in improving performance. 

Exploiting the relationships among data is a classical query optimization technique. As persistent data is increasingly being created and maintained programmatically, prior work that infers data relationships from data statistics misses an important opportunity. We present Coco, the first tool that identifies data relationships by analyzing database-backed applications. Once identified, Coco leverages the constraints to optimize the application's physical design and query execution. Instead of developing a fixed set of predefined rewriting rules, Coco employs an enumerate-test-verify technique to automatically exploit the discovered data constraints to improve query execution. Each resulting rewrite is provably equivalent to the original query. Using 14 real-world web applications, our experiments show that Coco can discover numerous data constraints from code analysis and improve real-world application performance significantly. 

Modern web applications have stringent latency requirements while processing an ever-increasing amount of user data. To address these challenges and improve programmer productivity, Object Relational Mapping (ORM) frameworks have been developed to allow developers writing database processing code in an object-oriented manner. Despite such frameworks, prior work found that developers still struggle in developing performant ORM-based web applications. This paper presents PowerStation, a RubyMine IDE plugin for optimizing web applications developed using the Ruby on Rails ORM. Using automated static analysis, PowerStation detects ORMrelated inefficiency problems and suggests fixes to developers. Our evaluation on 12 real-world applications shows that PowerStation can automatically detects 1221 performance issues across all of them. We have uploaded a tutorial on using PowerStation plugin to https://youtu.be/v_uY5bjGuK0. 

Many web applications use databases for persistent data storage, and using Object Relational Mapping (ORM) frameworks is a common way to develop such database-backed web applications. Unfortunately, developing efficient ORM applications is challenging, as the ORM framework hides the underlying database query generation and execution. This problem is becoming more severe as these applications need to process an increasingly large amount of persistent data. Recent research has targeted specific aspects of performance problems in ORM applications. However, there has not been any systematic study to identify common performance anti-patterns in real-world such applications, how they affect resulting application performance, and remedies for them. In this paper, we try to answer these questions through a comprehensive study of 12 representative real-world ORM applications. We generalize 9 ORM performance anti-patterns from more than 200 performance issues that we obtain by studying their bug-tracking systems and profiling their latest versions. To prove our point, we manually fix 64 performance issues in their latest versions and obtain a median speedup of 2× (and up to 39× max) with fewer than 5 lines of code change in most cases. Many of the issues we found have been confirmed by developers, and we have implemented ways to identify other code fragments with similar issues as well.