Object-Oriented Memory Reference Trace File Generation

A report submitted by Yudi Gondokaryono to the Research and Creative Productions Committee in 2006 on memory reference traces for Object Oriented Programs in an enhanced DINERO style format

Using Virtual Load/Store Queues (VLSQs) to Reduce the Negative Effects of Reordered Memory Instructions

The use of large instruction windows coupled with aggressive out-of order and prefetching capabilities has provided significant improvements in processor performance. In this paper, we quantify the effects of increased out-of-order aggressiveness on a processor’s memory ordering/consistency model as well as an application’s cache behavior. We observe that increasing reorder buffer sizes cause less than one third of issued memory instructions to be executed in actual program order. We show that increasing the reorder buffer size from 80 to 512 entries results in an increase in the frequency of memory traps by a factor of six and an increase in total execution overhead by 10–40%. Additionally, we observe that the reordering of memory instructions increases the L1 data cache accesses by 10–60% and the L1 data cache misses by 10–20%. These findings reveal that increased out-of-order capability can waste energy in two ways. First, re-fetching and re-executing instructions flushed due to traps require the fetch, map, and execution units to dissipate energy on work that has already been done before. Second, an increase in the number of cache accesses and cache misses needlessly dissipates energy. Both these side effects can be related to the reordering of memory instructions. Thus, to avoid wasting both energy and performance, we propose a virtual load/ store queue (VLSQ) within the existing physical load/store queue. The VLSQ reduces the reordering of memory instructions by limiting the number of memory instructions visible to the select and issue logic. We show that VLSQs can reduce trap overhead, cache accesses, and cache misses by as much as 45%, 50%, and 15% respectively when compared to traditional load/store queues. We observe that these reductions yield net power savings of 10–50% with degradation in performance by 1–5%

Architecting Memory Systems for Emerging Technologies

The advance of traditional dynamic random access memory (DRAM) technology has slowed down, while the capacity and performance needs of memory system have continued to increase. This is a result of increasing data volume from emerging applications, such as machine learning and big data analytics. In addition to such demands, increasing energy consumption is becoming a major constraint on the capabilities of computer systems. As a result, emerging non-volatile memories, for example, Spin Torque Transfer Magnetic RAM (STT-MRAM), and new memory interfaces, for example, High Bandwidth Memory (HBM), have been developed as an alternative. Thus far, most previous studies have retained a DRAM-like memory architecture and management policy. This preserves compatibility but hides the true benefits of those new memory technologies. In this research, we proposed the co-design of memory architectures and their management policies for emerging technologies. First, we introduced a new memory architecture for an STT-MRAM main memory. In particular, we defined a new page mode operation for efficient activation and sensing. By fully exploiting the non-destructive nature of STT- MRAM, our design achieved higher performance, lower energy consumption, and a smaller area than the traditional designs. Second, we developed a cost-effective technique to improve load balancing for HBM memory channels. We showed that the proposed technique was capable of efficiently redistributing memory requests across multiple memory channels to improve the channel utilization, resulting in improved performance.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145988/1/bcoh_1.pd

Doctor of Philosophy

dissertationIn recent years, a number of trends have started to emerge, both in microprocessor and application characteristics. As per Moore's law, the number of cores on chip will keep doubling every 18-24 months. International Technology Roadmap for Semiconductors (ITRS) reports that wires will continue to scale poorly, exacerbating the cost of on-chip communication. Cores will have to navigate an on-chip network to access data that may be scattered across many cache banks. The number of pins on the package, and hence available off-chip bandwidth, will at best increase at sublinear rate and at worst, stagnate. A number of disruptive memory technologies, e.g., phase change memory (PCM) have begun to emerge and will be integrated into the memory hierarchy sooner than later, leading to non-uniform memory access (NUMA) hierarchies. This will make the cost of accessing main memory even higher. In previous years, most of the focus has been on deciding the memory hierarchy level where data must be placed (L1 or L2 caches, main memory, disk, etc.). However, in modern and future generations, each level is getting bigger and its design is being subjected to a number of constraints (wire delays, power budget, etc.). It is becoming very important to make an intelligent decision about where data must be placed within a level. For example, in a large non-uniform access cache (NUCA), we must figure out the optimal bank. Similarly, in a multi-dual inline memory module (DIMM) non uniform memory access (NUMA) main memory, we must figure out the DIMM that is the optimal home for every data page. Studies have indicated that heterogeneous main memory hierarchies that incorporate multiple memory technologies are on the horizon. We must develop solutions for data management that take heterogeneity into account. For these memory organizations, we must again identify the appropriate home for data. In this dissertation, we attempt to verify the following thesis statement: "Can low-complexity hardware and OS mechanisms manage data placement within each memory hierarchy level to optimize metrics such as performance and/or throughput?" In this dissertation we argue for a hardware-software codesign approach to tackle the above mentioned problems at different levels of the memory hierarchy. The proposed methods utilize techniques like page coloring and shadow addresses and are able to handle a large number of problems ranging from managing wire-delays in large, shared NUCA caches to distributing shared capacity among different cores. We then examine data-placement issues in NUMA main memory for a many-core processor with a moderate number of on-chip memory controllers. Using codesign approaches, we achieve efficient data placement by modifying the operating system's (OS) page allocation algorithm for a wide variety of main memory architectures

Power considerations for memory-related microarchitecture designs

The fast performance improvement of computer systems in the last decade comes with the consistent increase on power consumption. In recent years, power dissipation is becoming a design constraint even for high-performance systems. Higher power dissipation means higher packaging and cooling cost, and lower reliability. This Ph.D. dissertation will investigate several memory-related design and optimization issues of general-purpose computer microarchitectures, aiming at reducing the power consumption without sacrificing the performance. The memory system consumes a large percentage of the system\u27s power. In addition, its behavior affects the processor power consumption significantly. In this dissertation, we propose two schemes to address the power-aware architecture issues related to memory: (1) We develop and evaluate low-power techniques for high-associativity caches. By dynamically applying different access modes for cache hits and misses, our proposed cache structure can achieve nearly lowest power consumption with minimal performance penalty. (2) We propose and evaluate look-ahead architectural adaptation techniques to reduce power consumption in processor pipelines based on the memory access information. The scheme can significantly reduce the power consumption of memory-intensive applications. Combined with other adaptation techniques, our schemes can effectively reduce the power consumption for both computer- and memory-intensive applications. The significance, potential impacts, and contributions of this dissertation are: (1) Academia and industry R & D has solely targeted the objective of high performance in both hardware and software designs since the beginning stage of building computer systems. However, the pursuit of high performance without considering energy consumption will inevitably lead to increased power dissipation and thus will eventually limit the development and progress of increasingly demanded mobile, portable, and high-performance computing systems. (2) Since our proposed method adaptively combines the merits of existing low-power cache designs, it approaches the optimum in terms of both retaining performance and saving energy. This low power solution for highly associative caches can be easily deployed with a low cost. (3) Using a cache miss , a common program execution event, as a triggering signal to slow down the processor issue rate, our scheme can effectively reduce processor power consumption. This design can be easily and practically deployed in many processor architectures with a low cost