Resource-aware scheduling for 2D/3D multi-/many-core processor-memory systems

This dissertation addresses the complexities of 2D/3D multi-/many-core processor-memory systems, focusing on two key areas: enhancing timing predictability in real-time multi-core processors and optimizing performance within thermal constraints. The integration of an increasing number of transistors into compact chip designs, while boosting computational capacity, presents challenges in resource contention and thermal management. The first part of the thesis improves timing predictability. We enhance shared cache interference analysis for set-associative caches, advancing the calculation of Worst-Case Execution Time (WCET). This development enables accurate assessment of cache interference and the effectiveness of partitioned schedulers in real-world scenarios. We introduce TCPS, a novel task and cache-aware partitioned scheduler that optimizes cache partitioning based on task-specific WCET sensitivity, leading to improved schedulability and predictability. Our research explores various cache and scheduling configurations, providing insights into their performance trade-offs. The second part focuses on thermal management in 2D/3D many-core systems. Recognizing the limitations of Dynamic Voltage and Frequency Scaling (DVFS) in S-NUCA many-core processors, we propose synchronous thread migrations as a thermal management strategy. This approach culminates in the HotPotato scheduler, which balances performance and thermal safety. We also introduce 3D-TTP, a transient temperature-aware power budgeting strategy for 3D-stacked systems, reducing the need for Dynamic Thermal Management (DTM) activation. Finally, we present 3QUTM, a novel method for 3D-stacked systems that combines core DVFS and memory bank Low Power Modes with a learning algorithm, optimizing response times within thermal limits. This research contributes significantly to enhancing performance and thermal management in advanced processor-memory systems

A survey on scheduling and mapping techniques in 3D Network-on-chip

Network-on-Chips (NoCs) have been widely employed in the design of multiprocessor system-on-chips (MPSoCs) as a scalable communication solution. NoCs enable communications between on-chip Intellectual Property (IP) cores and allow those cores to achieve higher performance by outsourcing their communication tasks. Mapping and Scheduling methodologies are key elements in assigning application tasks, allocating the tasks to the IPs, and organising communication among them to achieve some specified objectives. The goal of this paper is to present a detailed state-of-the-art of research in the field of mapping and scheduling of applications on 3D NoC, classifying the works based on several dimensions and giving some potential research directions

A State of Art Survey for OS Performance Improvement

Through the huge growth of heavy computing applications which require a high level of performance, it is observed that the interest of monitoring operating system performance has also demanded to be grown widely. In the past several years since OS performance has become a critical issue, many research studies have been produced to investigate and evaluate the stability status of OSs performance. This paper presents a survey of the most important and state of the art approaches and models to be used for performance measurement and evaluation. Furthermore, the research marks the capabilities of the performance-improvement of different operating systems using multiple metrics. The selection of metrics which will be used for monitoring the performance depends on monitoring goals and performance requirements. Many previous works related to this subject have been addressed, explained in details, and compared to highlight the top important features that will very beneficial to be depended for the best approach selection

A Study on Performance and Power Efficiency of Dense Non-Volatile Caches in Multi-Core Systems

In this paper, we present a novel cache design based on Multi-Level Cell Spin-Transfer Torque RAM (MLC STTRAM) that can dynamically adapt the set capacity and associativity to use efficiently the full potential of MLC STTRAM. We exploit the asymmetric nature of the MLC storage scheme to build cache lines featuring heterogeneous performances, that is, half of the cache lines are read-friendly, while the other is write-friendly. Furthermore, we propose to opportunistically deactivate ways in underutilized sets to convert MLC to Single-Level Cell (SLC) mode, which features overall better performance and lifetime. Our ultimate goal is to build a cache architecture that combines the capacity advantages of MLC and performance/energy advantages of SLC. Our experiments show an improvement of 43% in total numbers of conflict misses, 27% in memory access latency, 12% in system performance, and 26% in LLC access energy, with a slight degradation in cache lifetime (about 7%) compared to an SLC cache

Modeling and optimization of high-performance many-core systems for energy-efficient and reliable computing

Thesis (Ph.D.)--Boston UniversityMany-core systems, ranging from small-scale many-core processors to large-scale high performance computing (HPC) data centers, have become the main trend in computing system design owing to their potential to deliver higher throughput per watt. However, power densities and temperatures increase following the growth in the performance capacity, and bring major challenges in energy efficiency, cooling costs, and reliability. These challenges require a joint assessment of performance, power, and temperature tradeoffs as well as the design of runtime optimization techniques that monitor and manage the interplay among them. This thesis proposes novel modeling and runtime management techniques that evaluate and optimize the performance, energy, and reliability of many-core systems. We first address the energy and thermal challenges in 3D-stacked many-core processors. 3D processors with stacked DRAM have the potential to dramatically improve performance owing to lower memory access latency and higher bandwidth. However, the performance increase may cause 3D systems to exceed the power budgets or create thermal hot spots. In order to provide an accurate analysis and enable the design of efficient management policies, this thesis introduces a simulation framework to jointly analyze performance, power, and temperature for 3D systems. We then propose a runtime optimization policy that maximizes the system performance by characterizing the application behavior and predicting the operating points that satisfy the power and thermal constraints. Our policy reduces the energy-delay product (EDP) by up to 61.9% compared to existing strategies. Performance, cooling energy, and reliability are also critical aspects in HPC data centers. In addition to causing reliability degradation, high temperatures increase the required cooling energy. Communication cost, on the other hand, has a significant impact on system performance in HPC data centers. This thesis proposes a topology-aware technique that maximizes system reliability by selecting between workload clustering and balancing. Our policy improves the system reliability by up to 123.3% compared to existing temperature balancing approaches. We also introduce a job allocation methodology to simultaneously optimize the communication cost and the cooling energy in a data center. Our policy reduces the cooling cost by 40% compared to cooling-aware and performance-aware policies, while achieving comparable performance to performance-aware policy

Design Space Exploration and Resource Management of Multi/Many-Core Systems

The increasing demand of processing a higher number of applications and related data on computing platforms has resulted in reliance on multi-/many-core chips as they facilitate parallel processing. However, there is a desire for these platforms to be energy-efficient and reliable, and they need to perform secure computations for the interest of the whole community. This book provides perspectives on the aforementioned aspects from leading researchers in terms of state-of-the-art contributions and upcoming trends

Variation-Tolerant Non-Uniform 3D Cache Management in Memory Stacked Multi-Core Processors

Process variations in integrated circuits have significant impact on their performance, leakage and stability. This is particularly evident in large, regular and dense structures such as DRAMs. DRAMs are built using minimized transistors with presumably uniform speed in an organized array structure. Process variation can introduce latency disparity among different memory arrays. With the proliferation of 3D stacking technology, DRAMs become a favorable choice for stacking on top of a multi-core processor as a last level cache for large capacity, high bandwidth, and low power. Hence, variations in bank speed create a unique problem of non-uniform cache accesses in the 3D space.In this thesis, we investigate cache management techniques for tolerating process variation in a 3D DRAM stacked onto a multi-core processor. We modeled the process variation in a 4-layer DRAM memory to characterize the latency variations among different banks. As a result, the notion of fast and slow banks from the core's standpoint is no longer associated with their physical distances with the banks. They are determined by the different bank latencies due to process variation. We develop cache migration schemes that utilize fast banks while limiting the cost due to migration. Our experiments show that there is a great performance benefit in exploiting fast memory banks through migration. On average, a variation-aware management can improve the performance of a workload over the baseline (where the speed of the slowest bank is assumed for all banks) by 17.8%. We are also only 0.45% away in performance from an ideal memory where no PV is present

Memory region: a system abstraction for managing the complex memory structures of multicore platforms

The performance of modern many-core systems depends on the effective use of their complex cache and memory structures, and this will likely become more pronounced with the impending arrival of on-chip 3D stacked and non-volatile off-chip byte-addressable memory. Yet to date, operating systems have not treated memory as a first class schedulable resource, embracing memory heterogeneity. This dissertation presents a new software abstraction, called ‘memory region’, which denotes the current set of physical memory pages actively used by workloads. Using this abstraction, memory resources can be scheduled for applications to fully exploit a platform's underlying cache and memory system, thereby gaining improved performance and predictability in execution, particularly for the consolidated workloads seen in virtualized and cloud computing infrastructures. The abstraction's implementation in the Xen hypervisor involves the run-time detection of memory regions, the scheduled mapping of these regions to caches to match performance goals, and maintaining region-to-cache mappings using per-cache page tables. This dissertation makes the following specific contributions. First, its region scheduling method proposes that the location of memory blocks rather than CPU utilization is the principal determinant where workloads are run. It proposes a new scheduling method, the region scheduling that the location of memory blocks determines where the workloads are run. Second, treating memory blocks as first-class resources, new methods for efficient cache management are shown to improve application performance as well as the performance of certain operating system functions. Third, explicit memory scheduling makes it possible to disaggregate operating systems, without the need to change OS sources and with only small markups of target guest OS functionality. With this method, OS functions can be mapped to specific desired platform components, such as file system confined to running on specific cores and using only certain memory resources designated for its use. This can improve performance for applications heavily dependent on certain OS functions, by dynamically providing those functions with the resources needed for their current use, and it can prevent performance-critical application functionality from being needlessly perturbed by OS functions used for other purposes or by other jobs. Fourth, extensions of region scheduling can also help applications deal with the heterogeneous memory resources present in future systems, including on-chip stacked DRAM and NUMA or even NVRAM memory modules. More generally, regions scheduling is shown to apply to memory structures with well-defined differences in memory access latencies.Ph.D

Get Out of the Valley: Power-Efficient Address Mapping for GPUs

GPU memory systems adopt a multi-dimensional hardware structure to provide the bandwidth necessary to support 100s to 1000s of concurrent threads. On the software side, GPU-compute workloads also use multi-dimensional structures to organize the threads. We observe that these structures can combine unfavorably and create significant resource imbalance in the memory subsystem causing low performance and poor power-efficiency. The key issue is that it is highly application-dependent which memory address bits exhibit high variability. To solve this problem, we first provide an entropy analysis approach tailored for the highly concurrent memory request behavior in GPU-compute workloads. Our window-based entropy metric captures the information content of each address bit of the memory requests that are likely to co-exist in the memory system at runtime. Using this metric, we find that GPU-compute workloads exhibit entropy valleys distributed throughout the lower order address bits. This indicates that efficient GPU-address mapping schemes need to harvest entropy from broad address-bit ranges and concentrate the entropy into the bits used for channel and bank selection in the memory subsystem. This insight leads us to propose the Page Address Entropy (PAE) mapping scheme which concentrates the entropy of the row, channel and bank bits of the input address into the bank and channel bits of the output address. PAE maps straightforwardly to hardware and can be implemented with a tree of XOR-gates. PAE improves performance by 1.31 x and power-efficiency by 1.25 x compared to state-of-the-art permutation-based address mapping