Abdul Naeem

Memory Architecture and Management in an NoC Platform

The memory organization and the management of the memory space is a critical part of every NoC based platform design. We propose a Data Management Engine (DME), that is a block of programmable hardware and part of every processing element. It off-loads the processing element (CPU, DSP, etc.) by managing the memory space, memory access and the communication over the on-chip network. The DME’s main functions are virtual address translation, private and shared memory management, cache coherence protocol, support for memory consistency models, synchronization and protection mechanisms for shared memory communication. The DME is fully programmable and configurable thus allowing for customized support for high level data management functions such as dynamic memory allocation and abstract data types. This chapter describes the main concepts, design and functionality of the DME and presents case studies illustrating its usage and performance.

Realization and performance comparison of sequential and weak memory consistency models in network-on-chip based multi-core systems

This paper studies realization and performance comparison of the sequential and weak consistency models in the network-on-chip (NoC) based distributed shared memory (DSM) multi-core systems. Memory consistency constrains the order of shared memory operations for the expected behavior of the multi-core systems. Both the consistency models are realized in the NoC based multi-core systems. The performance of the two consistency models are compared for various sizes of networks using regular mesh topologies and deflection routing algorithm. The results show that the weak consistency improves the performance by 46.17% and 33.76% on average in the code and consistency latencies over the sequential consistency model, due to relaxation in the program order, as the system grows from single core to 64 cores.

Scalability of relaxed consistency models in NoC based multicore architectures

This paper studies realization of relaxed memory consistency models in the network-on-chip based distributed shared memory (DSM) multi-core systems. Within DSM systems, memory consistency is a critical issue since it affects not only the performance but also the correctness of programs. We investigate the scalability of the relaxed consistency models (weak, release consistency) implemented by using transaction counters. Our experimental results compare the average and maximum code, synchronization and data latencies of the two consistency models for various network sizes with regular mesh topologies. The observed latencies rise for both the consistency models as the network size grows. However, the scaling behaviors are different. With the release consistency model these latencies grow significantly slower than with the weak consistency due to better optimization potential by means of overlapping, reordering and program order relaxations. The release consistency improves the performance by 15.6% and 26.5% on average in the code and consistency latencies over the weak consistency model for the specific application, as the system grows from single core to 64 cores. The latency of data transactions grows 2.2 times faster on the average with a weak consistency model than with a release consistency model when the system scales from single core to 64 core

Scalability Analysis of Memory Consistency Models in NoC-Based Distributed Shared Memory SoCs

We analyze the scalability of six memory consistency models in network-on-chip (NoC)-based distributed shared memory multicore systems: 1) protected release consistency (PRC); 2) release consistency (RC); 3) weak consistency (WC); 4) partial store ordering (PSO); 5) total store ordering (TSO); and 6) sequential consistency (SC). Their realizations are based on a transaction counter and an address-stack-based approach. The scalability analysis is based on different workloads mapped on various sizes of networks using different problem sizes. For the experiments, we use Nostrum NoC-based configurable multicore platform with a 2-D mesh topology and a deflection routing algorithm. Under the synthetic workloads, the average execution time for the PRC, RC, WC, PSO, and TSO models in the 8 × 8 network (64-cores) is reduced by 32.3%, 28.3%, 20.1%, 13.8%, and 9.9% over the SC model, respectively. For the application workloads, as the network size grows, the average execution time under these relaxed memory models decreases with respect to the SC model depending on the application and its match to the architecture. The performance improvement of the PRC and RC models over the SC model tends to be higher than 50% as observed in the experiments, when the system is further scaled up. The area cost in the network interface for the relaxed memory models is increased by less than 4% over the SC model.

Scalability of weak consistency in NoC based multicore architectures

In Multicore Network-on-Chip, it is preferable to realize distributed but shared memory (DSM) in order to reuse the huge amount of legacy code and easy programming. Within DSM systems, memory consistency is a critical issue since it affects not only performance but also the correctness of programs. In this paper, we investigate the scalability of the weak consistency model, which may be implemented using a transaction counter. The experimental results compare synchronization latencies for various network sizes, topologies and lock positions in the network. Average synchronization latency rises exponentially for mesh and torus topologies as the network size grows. However, torus improves the synchronization latency in comparison to mesh. For mesh topology network average synchronization latency is also slightly affected by the lock position with respect to the network center.

Architecture Support and Comparison of Three Memory Consistency Models in NoC Based Systems

We propose a novel hardware support for three relaxed memory models, Release Consistency (RC), Partial Store Ordering (PSO) and Total Store Ordering (TSO) in Network-on-Chip (NoC) based distributed shared memory multicore systems. The RC model is realized by using a Transaction Counter and an Address Stack based approach to enforce the required global orders on the shared memory operations. The PSO and TSO models are realized by using a Write Transaction Counter and a Write Address Stack based approach to enforce the required global orders on the shared memory operations. In the experiments, we use a configurable platform based on a 2D mesh NoC using deflection routing policy. The results show that under synthetic workloads, the average execution time for the RC, PSO and TSO models in 8×8 network (64 cores) is reduced by 35.8%, 22.7% and 16.5% over the sequential consistency (SC) model, respectively. The average speedup for the RC, PSO and TSO models in 8×8 network under different application workloads is increased by 34.3%, 10.6% and 8.9% over the SC model, respectively. The area cost for the TSO, PSO and RC models is increased by less than 2% over the SC model at the interface to the processor.

Realization and Scalability of Release and Protected Release Consistency Models in NoC Based Systems

This paper studies the realization and scalability of release and protected release consistency models in Network-on-Chip (NoC) based Distributed Shared Memory (DSM) multi-core systems. The protected release consistency (PRC) model is proposed as an extension of the release consistency (RC) model and provides further relaxation in the shared memory operations. The realization schemes of RC and PRC models use a transaction counter in each node of the NoC based multi-core (McNoC) systems. Further, we study the scalability of these RC and PRC models and evaluate their performance in the McNoC platform. A configurable NoC based platform with 2D mesh topology and deflection routing algorithm is used in the tests. We experiment both with synthetic and application workloads. The performance of the RC and PRC models are compared using sequential consistency (SC) as the baseline. The experiments show that the average code execution time for the PRC model in 8x8 network (64 cores) is reduced by 30.5% over SC, and by 6.5% over RC model. Average data execution time in the 8x8 network for the PRC model is reduced by almost 37% over SC and by 8.8% over RC. The increase in area for the PRC of RC is about 880 gates in the network interface ( 1.7% ).

Scalability analysis of release and sequential consistency models in NoC based multicore systems

We analyze the scalability of the Release Consistency (RC) and Sequential Consistency (SC) models which are realized in the Network-on-Chip (NoC) based distributed shared memory multicore systems. The analysis is performed on the basis of workloads mapped on the different sizes of networks with different data sets. The experiments use a configurable platform based on a 2D mesh NoC using deflection routing algorithm. The results show that under the synthetic workloads using different distributed locks, the performance of the RC model is increased by 17.6% to 54.6% over the SC model in the 64-cores system. For the application workloads, as the network size grows from 1 to 64 cores, the execution time under the RC model decreases relative to the SC model which depends on the application and its match to the architecture. The performance improvement of the RC model over the SC model tends to be higher than 50% observed in the experiments, when the system is further scaled up.