Locality-oblivious cache organization leveraging single-cycle multi-hop NoCs

Locality has always been a critical factor in on-chip data placement on CMPs as accessing further-away caches has in the past been more costly than accessing nearby ones. Substantial research on locality-aware designs have thus focused on keeping a copy of the data private. However, this complicatesthe problem of data tracking and search/invalidation; tracking the state of a line at all on-chip caches at a directory or performing full-chip broadcasts are both non-scalable and extremely expensive solutions. In this paper, we make the case for Locality-Oblivious Cache Organization (LOCO), a CMP cache organization that leverages the on-chip network to create virtual single-cycle paths between distant caches, thus redefining the notion of locality. LOCO is a clustered cache organization, supporting both homogeneous and heterogeneous cluster sizes, and provides near single-cycle accesses to data anywhere within the cluster, just like a private cache. Globally, LOCO dynamically creates a virtual mesh connecting all the clusters, and performs an efficient global data search and migration over this virtual mesh, without having to resort to full-chip broadcasts or perform expensive directory lookups. Trace-driven and full system simulations running SPLASH-2 and PARSEC benchmarks show that LOCO improves application run time by up to 44.5% over baseline private and shared cache.Semiconductor Research CorporationUnited States. Defense Advanced Research Projects Agency (Semiconductor Technology Advanced Research Network

Scalability of broadcast performance in wireless network-on-chip

Networks-on-Chip (NoCs) are currently the paradigm of choice to interconnect the cores of a chip multiprocessor. However, conventional NoCs may not suffice to fulfill the on-chip communication requirements of processors with hundreds or thousands of cores. The main reason is that the performance of such networks drops as the number of cores grows, especially in the presence of multicast and broadcast traffic. This not only limits the scalability of current multiprocessor architectures, but also sets a performance wall that prevents the development of architectures that generate moderate-to-high levels of multicast. In this paper, a Wireless Network-on-Chip (WNoC) where all cores share a single broadband channel is presented. Such design is conceived to provide low latency and ordered delivery for multicast/broadcast traffic, in an attempt to complement a wireline NoC that will transport the rest of communication flows. To assess the feasibility of this approach, the network performance of WNoC is analyzed as a function of the system size and the channel capacity, and then compared to that of wireline NoCs with embedded multicast support. Based on this evaluation, preliminary results on the potential performance of the proposed hybrid scheme are provided, together with guidelines for the design of MAC protocols for WNoC.Peer ReviewedPostprint (published version

Adaptive memory-side last-level GPU caching

Emerging GPU applications exhibit increasingly high computation demands which has led GPU manufacturers to build GPUs with an increasingly large number of streaming multiprocessors (SMs). Providing data to the SMs at high bandwidth puts significant pressure on the memory hierarchy and the Network-on-Chip (NoC). Current GPUs typically partition the memory-side last-level cache (LLC) in equally-sized slices that are shared by all SMs. Although a shared LLC typically results in a lower miss rate, we find that for workloads with high degrees of data sharing across SMs, a private LLC leads to a significant performance advantage because of increased bandwidth to replicated cache lines across different LLC slices. In this paper, we propose adaptive memory-side last-level GPU caching to boost performance for sharing-intensive workloads that need high bandwidth to read-only shared data. Adaptive caching leverages a lightweight performance model that balances increased LLC bandwidth against increased miss rate under private caching. In addition to improving performance for sharing-intensive workloads, adaptive caching also saves energy in a (co-designed) hierarchical two-stage crossbar NoC by power-gating and bypassing the second stage if the LLC is configured as a private cache. Our experimental results using 17 GPU workloads show that adaptive caching improves performance by 28.1% on average (up to 38.1%) compared to a shared LLC for sharing-intensive workloads. In addition, adaptive caching reduces NoC energy by 26.6% on average (up to 29.7%) and total system energy by 6.1% on average (up to 27.2%) when configured as a private cache. Finally, we demonstrate through a GPU NoC design space exploration that a hierarchical two-stage crossbar is both more power- and area-efficient than full and concentrated crossbars with the same bisection bandwidth, thus providing a low-cost cooperative solution to exploit workload sharing behavior in memory-side last-level caches

Investigating Opportunities and Challenges in Modeling and Designing Scale-Out DNN Accelerators

The rapid growth of deep learning used in practical applications such as speech recognition, computer vision, natural language processing, robotics, any many other fields has opened the gate to new technology possibilities. Unfortunately, traditional hardware systems are being stretched to the maximum to accommodate the intense workloads presented by state-of-the-art deep learning processes in a time when transistor technology is not scaling. To serve the demand for better computational power and more specialized computations, specialized hardware needs to be developed that provides better latency and bandwidth specifications for various demanding applications. The trend in the semi-conductor industry is to move towards heterogenous System-On Chip (SoC) thereby choosing application specific performance vs. generality seen in most CPU architectures today. In most situations, hardware engineers are left to construct systems that serve the needs of various applications, often needing to predict the use-cases of the system. As with any field, the ability to predict and act on the future innovation trends of the industry is the difference between success and failure. A novel simulator for the design of convolutional neural network accelerators is presented and described in detail named SCALE-Sim (Systolic CNN Accelerator Simulator). The simulator is available as an open-sourced repository and has 2 primary use-cases in which computer architects can extract significant results. The first use-case is for system designers who would like to integrate an existing DNN accelerator architecture into a larger SoC and would be interested in system-level characterization results. The second use-case is for an accelerator architect who would like to use the tool to explore the accelerator design space by sweeping through design parameters.M.S

Rethinking Cache Hierarchy And Interconnect Design For Next-Generation Gpus

To match the increasing computational demands of GPGPU applications and to improve peak compute throughput, the core counts in GPUs have been increasing with every generation. However, the famous memory wall is a major performance determinant in GPUs. In other words, in most cases, peak throughput in GPUs is ultimately dictated by memory bandwidth. Therefore, to serve the memory demands of thousands of concurrently executing threads, GPUs are equipped with several sources of bandwidth such as on-chip private/shared caching resources and off-chip high bandwidth memories. However, the existing sources of bandwidth are often not sufficient for achieving optimal GPU performance. Therefore, it is important to conserve and improve memory bandwidth utilization. To achieve the aforementioned goal, this dissertation focuses on improving on-chip cache bandwidth by managing cache line (data) replication across L1 caches via rethinking the cache hierarchy and the interconnect design. Such data replication stems from the private nature of the L1 caches and inter-core locality. Specifically, each GPU core can independently request and store a given cache line (in its local L1 cache) while being oblivious to the previous requests of other cores. This dissertation treats inter-core locality (i.e., data replication) as a double-edged sword, and proposes the following. First, this dissertation shows that efficient inter-core communication can exploit data replication across the L1 caches to unlock an additional potential source of on-chip bandwidth, which we call as remote-core bandwidth. We propose to efficiently coordinate the data movement across GPU cores to exploit this remote-core bandwidth by investigating: a) which data is replicated across cores, b) which cores have the replicated data, and c) how to fetch the replicated data as soon as possible. Second, this dissertation shows that if data replication is eliminated (or reduced), then the L1 caches can effectively cache more data, leading to higher hit rates and more on-chip bandwidth. We propose designing a shared L1 cache organization, which restricts each core to cache only a unique slice of the address range, eliminating data replication. We develop lightweight mechanisms to: a) reduce the inter-core communication overheads and b) to identify applications that prefer the private L1 organization and hence execute them accordingly. Third, to improve the performance, area, and energy efficiency of the shared L1 organization, this dissertation proposes DC-L1 (DeCoupled-L1) cache, an L1 cache separated from the GPU core. We show how the decoupled nature of the DC-L1 caches provides an opportunity to aggregate the L1 caches, and enables low-overhead efficient data placement designs. These optimizations reduce data replication across the L1s and increase their bandwidth utilization. Altogether, this dissertation develops several innovative techniques to improve the efficiency of the GPU on-chip memory system, which are necessary to address the memory wall problem. The future work will explore other designs and techniques to improve on-chip bandwidth utilization by considering other bandwidth sources (e.g., scratchpad and L2 cache)