Heterogeneous Photonic Network-on-Chip with Dynamic Bandwidth Allocation

Advancements in the field of chip fabrication has facilitated in integrating more number of transistors in a given area which has lead to an era of multi-core processors. Future multi-core chips or chip multiprocessors (CMPs) will have hundreds of heterogeneous components including processing engines, custom logic, GPU units, programmable fabrics and distributed memory. Such multi-core chips are expected to run varied multiple parallel workloads simultaneously. Hence, different communicating cores will require different bandwidths leading to the necessity of a heterogeneous Network-on-Chip (NoC) architecture. Simply over-provisioning for performance will invariably result in loss of power efficiency. On the other hand, recent research has shown that photonic interconnects are capable of achieving high-bandwidth and energy-efficient on-chip data transfer. In this paper we propose a dynamic heterogeneous photonic NoC (d-HetPNOC) architecture with dynamic bandwidth allocation to achieve better performance and energy-efficiency compared to a homogeneous photonic NoC architecture with the same aggregate data bandwidth

Bandwidth Requirements of GPU Architectures

A new trend in chip multiprocessor (CMP) design is to incorporate graphics processing unit (GPU) cores, making them heterogeneous. GPU cores have a higher bandwidth requirement than CPU cores, as they tend to generate much more memory requests. In order to achieve good performance, there must be sufficient bandwidth between the GPU shader cores and main memory to service these memory requests in a timely manner. However, designing for the highest possible bandwidth will lead to high energy costs. The communication requirements of GPU cores must be determined in order to choose a proper interconnect. To this end, we have simulated several CUDA benchmarks with varying bandwidths using the GPGPU-Sim simulator. Our results show that the communication requirements of GPUs vary from workload to workload. We suggest that cores be connected using a photonic interconnect capable of supporting different bandwidths in order to reduce power consumption. For each transmission, the interconnect used will depend on how the bandwidth affects performance. We determined that the ratio of interconnect-shader stalls to the total number of execution cycles is a good indicator of whether or not an application will be bandwidth-sensitive. We used this finding to develop a bandwidth selection policy for GPU applications using a photonic NoC. With our policy selections, the photonic interconnect used 12.5% less power than a photonic interconnect with optimal performing choices, which only gave a performance improvement of 1.37% compared to our policy. The photonic interconnect with our policy also had the lowest energy-delay product out of the interconnects we compared it against

Automatic synthesis and optimization of chip multiprocessors

The microprocessor technology has experienced an enormous growth during the last decades. Rapid downscale of the CMOS technology has led to higher operating frequencies and performance densities, facing the fundamental issue of power dissipation. Chip Multiprocessors (CMPs) have become the latest paradigm to improve the power-performance efficiency of computing systems by exploiting the parallelism inherent in applications. Industrial and prototype implementations have already demonstrated the benefits achieved by CMPs with hundreds of cores.CMP architects are challenged to take many complex design decisions. Only a few of them are:- What should be the ratio between the core and cache areas on a chip?- Which core architectures to select?- How many cache levels should the memory subsystem have?- Which interconnect topologies provide efficient on-chip communication?These and many other aspects create a complex multidimensional space for architectural exploration. Design Automation tools become essential to make the architectural exploration feasible under the hard time-to-market constraints. The exploration methods have to be efficient and scalable to handle future generation on-chip architectures with hundreds or thousands of cores.Furthermore, once a CMP has been fabricated, the need for efficient deployment of the many-core processor arises. Intelligent techniques for task mapping and scheduling onto CMPs are necessary to guarantee the full usage of the benefits brought by the many-core technology. These techniques have to consider the peculiarities of the modern architectures, such as availability of enhanced power saving techniques and presence of complex memory hierarchies.This thesis has several objectives. The first objective is to elaborate the methods for efficient analytical modeling and architectural design space exploration of CMPs. The efficiency is achieved by using analytical models instead of simulation, and replacing the exhaustive exploration with an intelligent search strategy. Additionally, these methods incorporate high-level models for physical planning. The related contributions are described in Chapters 3, 4 and 5 of the document.The second objective of this work is to propose a scalable task mapping algorithm onto general-purpose CMPs with power management techniques, for efficient deployment of many-core systems. This contribution is explained in Chapter 6 of this document.Finally, the third objective of this thesis is to address the issues of the on-chip interconnect design and exploration, by developing a model for simultaneous topology customization and deadlock-free routing in Networks-on-Chip. The developed methodology can be applied to various classes of the on-chip systems, ranging from general-purpose chip multiprocessors to application-specific solutions. Chapter 7 describes the proposed model.The presented methods have been thoroughly tested experimentally and the results are described in this dissertation. At the end of the document several possible directions for the future research are proposed

Investigation of LSTM Based Prediction for Dynamic Energy Management in Chip Multiprocessors

In this paper, we investigate the effectiveness of using long short-term memory (LSTM) instead of Kalman filtering to do prediction for the purpose of constructing dynamic energy management (DEM) algorithms in chip multi-processors (CMPs). Either of the two prediction methods is employed to estimate the workload in the next control period for each of the processor cores. These estimates are then used to select voltage-frequency (VF) pairs for each core of the CMP during the next control period as part of a dynamic voltage and frequency scaling (DVFS) technique. The objective of the DVFS technique is to reduce energy consumption under performance constraints that are set by the user. We conduct our investigation using a custom Sniper system simulation framework. Simulation results for 16 and 64 core network-on-chip based CMP architectures and using several benchmarks demonstrate that the LSTM is slightly better than Kalman filtering

Investigation of LSTM Based Prediction for Dynamic Energy Management in Chip Multiprocessors

In this paper, we investigate the effectiveness of using long short-term memory (LSTM) instead of Kalman filtering to do prediction for the purpose of constructing dynamic energy management (DEM) algorithms in chip multi-processors (CMPs). Either of the two prediction methods is employed to estimate the workload in the next control period for each of the processor cores. These estimates are then used to select voltage-frequency (VF) pairs for each core of the CMP during the next control period as part of a dynamic voltage and frequency scaling (DVFS) technique. The objective of the DVFS technique is to reduce energy consumption under performance constraints that are set by the user. We conduct our investigation using a custom Sniper system simulation framework. Simulation results for 16 and 64 core network-on-chip based CMP architectures and using several benchmarks demonstrate that the LSTM is slightly better than Kalman filtering

A Survey of Prediction and Classification Techniques in Multicore Processor Systems

In multicore processor systems, being able to accurately predict the future provides new optimization opportunities, which otherwise could not be exploited. For example, an oracle able to predict a certain application\u27s behavior running on a smart phone could direct the power manager to switch to appropriate dynamic voltage and frequency scaling modes that would guarantee minimum levels of desired performance while saving energy consumption and thereby prolonging battery life. Using predictions enables systems to become proactive rather than continue to operate in a reactive manner. This prediction-based proactive approach has become increasingly popular in the design and optimization of integrated circuits and of multicore processor systems. Prediction transforms from simple forecasting to sophisticated machine learning based prediction and classification that learns from existing data, employs data mining, and predicts future behavior. This can be exploited by novel optimization techniques that can span across all layers of the computing stack. In this survey paper, we present a discussion of the most popular techniques on prediction and classification in the general context of computing systems with emphasis on multicore processors. The paper is far from comprehensive, but, it will help the reader interested in employing prediction in optimization of multicore processor systems

Exploiting Properties of CMP Cache Traffic in Designing Hybrid Packet/Circuit Switched NoCs

Chip multiprocessors with few to tens of processing cores are already commercially available. Increased scaling of technology is making it feasible to integrate even more cores on a single chip. Providing the cores with fast access to data is vital to overall system performance. When a core requires access to a piece of data, the core's private cache memory is searched first. If a miss occurs, the data is looked up in the next level(s) of the memory hierarchy, where often one or more levels of cache are shared between two or more cores. Communication between the cores and the slices of the on-chip shared cache is carried through the network-on-chip(NoC). Interestingly, the cache and NoC mutually affect the operation of each other; communication over the NoC affects the access latency of cache data, while the cache organization generates the coherence and data messages, thus affecting the communication patterns and latency over the NoC. This thesis considers hybrid packet/circuit switched NoCs, i.e., packet switched NoCs enhanced with the ability to configure circuits. The communication and performance benefit that come from using circuits is predicated on amortizing the time cost incurred for configuring the circuits. To address this challenge, NoC designs are proposed that take advantage of properties of the cache traffic, namely temporal locality and predictability, to amortize or hide the circuit configuration time cost. First, a coarse-grained circuit configuration policy is proposed that exploits the temporal locality in the cache traffic to periodically configure circuits for the heavily communicating nodes. This allows the design of a locality-aware cache that promotes temporal communication locality through data placement, while designing suitable data replacement and migration policies. Next, a fine-grained configuration policy, called Déjà Vu switching, is proposed for leveraging predictability of data messages by initiating a circuit configuration as soon as a cache hit is detected and before the data becomes available. Its benefit is demonstrated for saving interconnect energy in multi-plane NoCs. Finally, a more proactive configuration policy is proposed for fast caches, where circuit reservations are initiated by request messages, which can greatly improve communication latency and system performance