Vector-Processing for Mobile Devices: Benchmark and Analysis

Vector processing has become commonplace in today's CPU microarchitectures. Vector instructions improve performance and energy which is crucial for resource-constraint mobile devices. The research community currently lacks a comprehensive benchmark suite to study the benefits of vector processing for mobile devices. This paper presents Swan-an extensive vector processing benchmark suite for mobile applications. Swan consists of a diverse set of data-parallel workloads from four commonly used mobile applications: operating system, web browser, audio/video messaging application, and PDF rendering engine. Using Swan benchmark suite, we conduct a detailed analysis of the performance, power, and energy consumption of vectorized workloads, and show that: (a) Vectorized kernels increase the pressure on cache hierarchy due to the higher rate of memory requests. (b) Vector processing is more beneficial for workloads with lower precision operations and higher cache hit rates. (c) Limited Instruction-Level Parallelism and strided memory accesses to multi-dimensional data structures prevent vector processing benefits from scaling with more SIMD functional units and wider registers. (d) Despite lower computation throughput than domain-specific accelerators, such as GPU, vector processing outperforms these accelerators for kernels with lower operation counts. Finally, we show five common computation patterns in mobile data-parallel workloads that dominate the execution time.Comment: 2023 IEEE International Symposium on Workload Characterization (IISWC

Optimizing Emerging Graph Applications Using Hardware-Software Co-Design

A graph is a ubiquitous data structure that models entities and their interactions through the collec- tions of nodes and edges. It is widely employed in several important application domains ranging from social media, navigation tools, search engines, physics simulations, and biology. Despite its prevalence, the performance of graph algorithms on commercial platforms is limited. This is mainly due to the irregular memory accesses and convoluted control flow instructions used in graph algorithms while accessing large volumes of graph data (with billions of nodes/edges). Therefore, there is a pressing need for optimizing the performance of graph workloads. In this thesis, I present a systematic optimization study of a variety of graph workloads run- ning on both static and dynamic graphs. At a high level, I first analyze the unique challenges and execution bottlenecks of the state-of-the-art graph software frameworks running on commercial hardware platforms. I then use the insights obtained from this analysis to propose design optimiza- tions catered to the unique workload characteristics of a diversity of graph workloads. Specifically, first, I propose Prodigy—a hardware-software co-design solution to improve the performance of traditional graph processing algorithms (e.g., PageRank and SSSP) on multi-core CPUs. Second, I present an in-depth study of random walk–based graph learning algorithms on temporal graphs (a type of dynamic graph). Specifically, this study delivers high-performance, open-source CPU and GPU implementations of important graph learning applications, conducts a detailed performance analysis, and makes recommendations for future optimizations. Third, I showcase NDMiner—a domain-specialized Near Data Processing (NDP) architecture that signif- icantly improves the performance of Graph Pattern Mining (GPM) workloads. Last, I present Mint—a novel hardware accelerator architecture and an accompanying programming model for efficiently mining motifs in temporal graphs.PhDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/175618/1/talatin_1.pd