Sparse matrix-vector multiplication (SpMV) is a critical building block for iterative graph analytics algorithms. Typically, such algorithms have a varying active vertex set across iterations. This variability has been used to improve performance by either dynamically switching algorithms between iterations (software) or designing custom accelerators (hardware) for graph analytics algorithms. In this work, we propose a novel framework, CoSPARSE, that employs hardware and software reconfiguration as a synergistic solution to accelerate SpMV-based graph analytics algorithms. Building on previously proposed general-purpose reconfigurable hardware, we implement CoSPARSE as a software layer, abstracting the hardware as a specialized SpMV accelerator. CoSPARSE dynamically selects software and hardware configurations for each iteration and achieves a maximum speedup of 2.0 × compared to the naïve implementation with no reconfiguration. Across a suite of graph algorithms, CoSPARSE outperforms a state-of-the-art shared memory framework, Ligra, on a Xeon CPU with up to 3.51 × better performance and 877 × better energy efficiency.

Irregular workloads are typically bottlenecked by the memory system. These workloads often use sparse data representations, e.g., compressed sparse row/column (CSR/CSC), to conserve space at the cost of complicated, irregular traversals. Such traversals access large volumes of data and offer little locality for caches and conventional prefetchers to exploit. This paper presents Prodigy, a low-cost hardware-software codesign solution for intelligent prefetching to improve the memory latency of several important irregular workloads. Prodigy targets irregular workloads including graph analytics, sparse linear algebra, and fluid mechanics that exhibit two specific types of data-dependent memory access patterns. Prodigy adopts a “best of both worlds” approach by using static program information from software, and dynamic run-time information from hardware. The core of the system is the Data Indirection Graph (DIG)-a proposed compact representation used to express program semantics such as the layout and memory access patterns of key data structures. The DIG representation is agnostic to a particular data structure format and is demonstrated to work with several sparse formats including CSR and CSC. Program semantics are automatically captured with a compiler pass, encoded as a DIG, and inserted into the application binary. The DIG is then used to program a low-cost hardware prefetcher to fetch data according to an irregular algorithm's data structure traversal pattern. We equip the prefetcher with a flexible prefetching algorithm that maintains timeliness by dynamically adapting its prefetch distance to an application's execution pace. We evaluate the performance, energy consumption, and transistor cost of Prodigy using a variety of algorithms from the GAP, HPCG, and NAS benchmark suites. We compare the performance of Prodigy against a non-prefetching baseline as well as state-of-the-art prefetchers. We show that by using just 0.8KB of storage, Prodigy outperforms a non-prefetching baseline by $2.6 \times$ and saves energy by $1.6 \times$, on average. Prodigy also outperforms modern data prefetchers by $1.5- 2.3 \times$.

With the end of Dennard scaling and Moore's law, it is becoming increasingly difficult to build hardware for emerging applications that meet power and performance targets, while remaining flexible and programmable for end users. This is particularly true for domains that have frequently changing algorithms and applications involving mixed sparse/dense data structures, such as those in machine learning and graph analytics. To overcome this, we present a flexible accelerator called Transmuter, in a novel effort to bridge the gap between General-Purpose Processors (GPPs) and Application-Specific Integrated Circuits (ASICs). Transmuter adapts to changing kernel characteristics, such as data reuse and control divergence, through the ability to reconfigure the on-chip memory type, resource sharing and dataflow at run-time within a short latency. This is facilitated by a fabric of light-weight cores connected to a network of reconfigurable caches and crossbars. Transmuter addresses a rapidly growing set of algorithms exhibiting dynamic data movement patterns, irregularity, and sparsity, while delivering GPU-like efficiencies for traditional dense applications. Finally, in order to support programmability and ease-of-adoption, we prototype a software stack composed of low-level runtime routines, and a high-level language library called TransPy, that cater to expert programmers and end-users, respectively.

Python is a popular, dynamic language for data science and scientific computing. To ensure efficiency, significant numerical libraries are implemented in static native languages. However, performance suffers when switching between native and non-native code, especially if data has to be converted between native arrays and Python data structures. As GPU accelerators are increasingly used, this problem becomes particularly acute. Data and control has to be repeatedly transferred between the accelerator and the host.

Graphics Processing Units (GPUs) critically rely on a complex system software stack comprising kernel- and user-space drivers and Just-in-time (JIT) compilers. Yet, existing GPU simulators typically abstract away details of the software stack and GPU instruction set. Partly, this is because GPU vendors rarely release sufficient information about their latest GPU products. However, this is also due to the lack of an integrated CPU/GPU simulation framework, which is complete and powerful enough to drive the complex GPU software environment. This has led to a situation where research on GPU architectures and compilers is largely based on outdated or greatly simplified architectures and software stacks, undermining the validity of the generated results. In this paper we develop a full-system system simulation environment for a mobile platform, which enables users to run a complete and unmodified software stack for a state-of-the-art mobile Arm CPU and Mali-G71 GPU powered device. We validate our simulator against a hardware implementation and Arm's stand-alone GPU simulator, achieving 100% architectural accuracy across all available toolchains. We demonstrate the capability of our GPU simulation framework by optimizing an advanced Computer Vision application using simulated statistics unavailable with other simulation approaches or physical GPU implementations. We demonstrate that performance optimizations for desktop GPUs trigger bottlenecks on mobile GPUs, and show the importance of efficient memory use.

Visual understanding of 3-D environments in real time, at low power, is a huge computational challenge. Often referred to as simultaneous localization and mapping (SLAM), it is central to applications spanning domestic and industrial robotics, autonomous vehicles, and virtual and augmented reality. This paper describes the results of a major research effort to assemble the algorithms, architectures, tools, and systems software needed to enable delivery of SLAM, by supporting applications specialists in selecting and configuring the appropriate algorithm and the appropriate hardware, and compilation pathway, to meet their performance, accuracy, and energy consumption goals. The major contributions we present are: 1) tools and methodology for systematic quantitative evaluation of SLAM algorithms; 2) automated, machine-learning-guided exploration of the algorithmic and implementation design space with respect to multiple objectives; 3) end-to-end simulation tools to enable optimization of heterogeneous, accelerated architectures for the specific algorithmic requirements of the various SLAM algorithmic approaches; and 4) tools for delivering, where appropriate, accelerated, adaptive SLAM solutions in a managed, JIT-compiled, adaptive runtime context.