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Sparse Matrix-Vector Multiplication (SpMV) is an essential sparse kernel. Numerous methods have been developed to accelerate SpMV. However, no single method consistently gives the highest performance across a wide range of matrices. For this reason, a performance prediction model is needed to predict the best SpMV method for a given sparse matrix. Unfortunately, predicting SpMV’s performance is challenging due to the diversity of factors that impact it. In this work, we develop a machine learning framework called WISE that accurately predicts the magnitude of the speedups of different SpMV methods over a baseline method for a given sparse matrix. WISE relies on a novel feature set that summarizes a matrix’s size, skew, and locality traits. WISE can then select the best SpMV method for each specific matrix. With a set of nearly 1,500 matrices, we show that using WISE delivers an average speedup of 2.4× over using Intel’s MKL in a 24-core server. 

The security of computers is at risk because of information leaking through their power consumption. Attackers can use advanced signal measurement and analysis to recover sensitive data from this side channel. To address this problem, this paper presents Maya, a simple and effective defense against power side channels. The idea is to use formal control to re-shape the power dissipated by a computer in an application-transparent manner—preventing attackers from learning any information about the applications that are running. With formal control, a controller can reliably keep power close to a desired target function even when runtime conditions change unpredictably. By selecting the target function intelligently, the controller can make power to follow any desired shape, appearing to carry activity information which, in reality, is unrelated to the application. Maya can be implemented in privileged software, firmware, or simple hardware. In this paper, we implement Maya on three machines using privileged threads only, and show its effectiveness and ease of deployment. Maya has already thwarted a newly-developed remote power attack. 

Dynamic voltage and frequency scaling (DVFS) is a well-known technique to reduce the power and/or energy consumption of various applications. While most processors provide chip-level DVFS, where the frequency and voltage of the cores in a chip can only be changed all together; core-level DVFS, where each core can be controlled independently, requires core-level voltage regulators in hardware and only is supported in production in Haswell generation among Intel processors. The finer grained control that per-core DVFS provides can lead to higher energy efficiency compared to chip-level DVFS especially for the unsynchronized, unstructured parallel applications when carefully applied. Ability to do per-core DVFS opens up new doors for different optimizations within runtime systems. We implement an intelligent energy efficient runtime module which uses a fine-grained function level per-core DVFS approach. Our module finds the energy-optimal frequency for each phase/function/kernel of the application over the first few iterations and applies the optimal frequency for each function. We test our implementation on Haswell processors and show that our algorithm enables 4% to 35% energy reduction over chip-level DVFS with as much as performance. 

Computing is taking a central role in advancing science, technology, and society, facilitated by increasingly capable systems. Computers are expected to perform a variety of tasks, including life-critical functions, while the resources they require (such as storage and energy) are becoming increasingly limited. To meet expectations, computers use control algorithms that monitor the requirements of the applications they run and reconfigure themselves in response. 

Resource control in heterogeneous computers built with subsystems from different vendors is challenging. There is a tension between the need to quickly generate local decisions in each subsystem and the desire to coordinate the different subsystems for global optimization. In practice, global coordination among subsystems is considered hard, and current commercial systems use centralized controllers. The result is high response time and high design cost due to lack of modularity. To control emerging heterogeneous computers effectively, we propose a new control framework called Tangram that is fast, glob- ally coordinated, and modular. Tangram introduces a new formal controller that combines multiple engines for optimization and safety, and has a standard interface. Building the controller for a subsystem requires knowing only about that subsystem. As a het- erogeneous computer is assembled, the controllers in the different subsystems are connected hierarchically, exchanging standard co- ordination signals. To demonstrate Tangram, we prototype it in a heterogeneous server that we assemble using components from multiple vendors. Compared to state-of-the-art control, Tangram re- duces, on average, the execution time of heterogeneous applications by 31% and their energy-delay product by 39%. 

Computer systems are operating in environments where applications are rapidly diversifying while resources like energy and storage are becoming severely limited. These environments demand that computers dynamically manage their resources efciently to deliver the best performance and meet many goals. An important challenge in designing computer resource management systems is that computers are structured in multiple modular layers, such as hardware, operating system, and network. Each layer is complex and designed independently without full knowledge of the other layers. Therefore, computers must have modular resource controllers for each layer that are robust to modeling limitations and the uncertainty of inuence from other layers. Existing designs either rely heavily on ad hoc heuristics or lack modularity. We present a design with multiple Structured Singular Value (SSV) controllers from robust control theory for systematic and efcient computer management. On a challenging computer, we build a two-layer SSV control system that signicantly outperforms state-of-the-art heuristics.