Exploiting Inter- and Intra-Memory Asymmetries for Data Mapping in Hybrid Tiered-Memories

Modern computing systems are embracing hybrid memory comprising of DRAM and non-volatile memory (NVM) to combine the best properties of both memory technologies, achieving low latency, high reliability, and high density. A prominent characteristic of DRAM-NVM hybrid memory is that it has NVM access latency much higher than DRAM access latency. We call this inter-memory asymmetry. We observe that parasitic components on a long bitline are a major source of high latency in both DRAM and NVM, and a significant factor contributing to high-voltage operations in NVM, which impact their reliability. We propose an architectural change, where each long bitline in DRAM and NVM is split into two segments by an isolation transistor. One segment can be accessed with lower latency and operating voltage than the other. By introducing tiers, we enable non-uniform accesses within each memory type (which we call intra-memory asymmetry), leading to performance and reliability trade-offs in DRAM-NVM hybrid memory. We extend existing NVM-DRAM OS in three ways. First, we exploit both inter- and intra-memory asymmetries to allocate and migrate memory pages between the tiers in DRAM and NVM. Second, we improve the OS's page allocation decisions by predicting the access intensity of a newly-referenced memory page in a program and placing it to a matching tier during its initial allocation. This minimizes page migrations during program execution, lowering the performance overhead. Third, we propose a solution to migrate pages between the tiers of the same memory without transferring data over the memory channel, minimizing channel occupancy and improving performance. Our overall approach, which we call MNEME, to enable and exploit asymmetries in DRAM-NVM hybrid tiered memory improves both performance and reliability for both single-core and multi-programmed workloads.Comment: 15 pages, 29 figures, accepted at ACM SIGPLAN International Symposium on Memory Managemen

성능과 용량 향상을 위한 적층형 메모리 구조

학위논문 (박사)-- 서울대학교 대학원 : 융합과학기술대학원 융합과학부(지능형융합시스템전공), 2019. 2. 안정호.The advance of DRAM manufacturing technology slows down, whereas the density and performance needs of DRAM continue to increase. This desire has motivated the industry to explore emerging Non-Volatile Memory (e.g., 3D XPoint) and the high-density DRAM (e.g., Managed DRAM Solution). Since such memory technologies increase the density at the cost of longer latency, lower bandwidth, or both, it is essential to use them with fast memory (e.g., conventional DRAM) to which hot pages are transferred at runtime. Nonetheless, we observe that page transfers to fast memory often block memory channels from servicing memory requests from applications for a long period. This in turn significantly increases the high-percentile response time of latency-sensitive applications. In this thesis, we propose a high-density managed DRAM architecture, dubbed 3D-XPath for applications demanding both low latency and high capacity for memory. 3D-XPath DRAM stacks conventional DRAM dies with high-density DRAM dies explored in this thesis and connects these DRAM dies with 3D-XPath. Especially, 3D-XPath allows unused memory channels to service memory requests from applications when primary channels supposed to handle the memory requests are blocked by page transfers at given moments, considerably increasing the high-percentile response time. This can also improve the throughput of applications frequently copying memory blocks between kernel and user memory spaces. Our evaluation shows that 3D-XPath DRAM decreases high-percentile response time of latency-sensitive applications by ∼30% while improving the throughput of an I/O-intensive applications by ∼39%, compared with DRAM without 3D-XPath. Recent computer systems are evolving toward the integration of more CPU cores into a single socket, which require higher memory bandwidth and capacity. Increasing the number of channels per socket is a common solution to the bandwidth demand and to better utilize these increased channels, data bus width is reduced and burst length is increased. However, this longer burst length brings increased DRAM access latency. On the memory capacity side, process scaling has been the answer for decades, but cell capacitance now limits how small a cell could be. 3D stacked memory solves this problem by stacking dies on top of other dies. We made a key observation in real multicore machine that multiple memory controllers are always not fully utilized on SPEC CPU 2006 rate benchmark. To bring these idle channels into play, we proposed memory channel sharing architecture to boost peak bandwidth of one memory channel and reduce the burst latency on 3D stacked memory. By channel sharing, the total performance on multi-programmed workloads and multi-threaded workloads improved up to respectively 4.3% and 3.6% and the average read latency reduced up to 8.22% and 10.18%.DRAM 제조 기술의 발전은 속도가 느려지는 반면 DRAM의 밀도 및 성능 요구는 계속 증가하고 있다. 이러한 요구로 인해 새로운 비 휘발성 메모리(예: 3D-XPoint) 및 고밀도 DRAM(예: Managed asymmetric latency DRAM Solution)이 등장하였다. 이러한 고밀도 메모리 기술은 긴 레이턴시, 낮은 대역폭 또는 두 가지 모두를 사용하는 방식으로 밀도를 증가시키기 때문에 성능이 좋지 않아, 핫 페이지를 고속 메모리(예: 일반 DRAM)로 스왑되는 저용량의 고속 메모리가 동시에 사용되는 것이 일반적이다. 이러한 스왑 과정에서 빠른 메모리로의 페이지 전송이 일반적인 응용프로그램의 메모리 요청을 오랫동안 처리하지 못하도록 하기 때문에, 대기 시간에 민감한 응용 프로그램의 백분위 응답 시간을 크게 증가시켜, 응답 시간의 표준 편차를 증가시킨다. 이러한 문제를 해결하기 위해 본 학위 논문에서는 저 지연시간 및 고용량 메모리를 요구하는 애플리케이션을 위해 3D-XPath, 즉 고밀도 관리 DRAM 아키텍처를 제안한다. 이러한 3D-톔소를 집적한 DRAM은 저속의 고밀도 DRAM 다이를 기존의 일반적인 DRAM 다이와 동시에 한 칩에 적층하고, DRAM 다이끼리는 제안하는 3D-XPath 하드웨어를 통해 연결된다. 이러한 3D-XPath는 핫 페이지 스왑이 일어나는 동안 응용프로그램의 메모리 요청을 차단하지 않고 사용량이 적은 메모리 채널로 핫 페이지 스왑을 처리 할 수 있도록 하여, 데이터 집중 응용 프로그램의 백분위 응답 시간을 개선시킨다. 또한 제안하는 하드웨어 구조를 사용하여, 추가적으로 O/S 커널과 유저 스페이스 간의 메모리 블록을 자주 복사하는 응용 프로그램의 처리량을 향상시킬 수 있다. 이러한 3D-XPath DRAM은 3D-XPath가 없는 DRAM에 비해 I/O 집약적인 응용프로그램의 처리량을 최대 39 % 향상시키면서 레이턴시에 민감한 응용 프로그램의 높은 백분위 응답 시간을 최대 30 %까지 감소시킬 수 있다. 또한 최근의 컴퓨터 시스템은 보다 많은 메모리 대역폭과 용량을 필요로하는 더 많은 CPU 코어를 단일 소켓으로 통합하는 방향으로 진화하고 있다. 이러한 소켓 당 채널 수를 늘리는 것은 대역폭 요구에 대한 일반적인 해결책이며, 최신의 DRAM 인터페이스의 발전 양상은 증가한 채널을 보다 잘 활용하기 위해 데이터 버스 폭이 감소되고 버스트 길이가 증가한다. 그러나 길어진 버스트 길이는 DRAM 액세스 대기 시간을 증가시킨다. 추가적으로 최신의 응용프로그램은 더 많은 메모리 용량을 요구하며, 미세 공정으로 메모리 용량을 증가시키는 방법론은 수십 년 동안 사용되었지만, 20 nm 이하의 미세공정에서는 더 이상 공정 미세화를 통해 메모리 밀도를 증가시키기가 어려운 상황이며, 적층형 메모리를 사용하여 용량을 증가시키는 방법을 사용한다. 이러한 상황에서, 실제 최신의 멀티코어 머신에서 SPEC CPU 2006 응용프로그램을 멀티코어에서 실행하였을 때, 항상 시스템의 모든 메모리 컨트롤러가 완전히 활용되지 않는다는 사실을 관찰했다. 이러한 유휴 채널을 사용하기 위해 하나의 메모리 채널의 피크 대역폭을 높이고 3D 스택 메모리의 버스트 대기 시간을 줄이기 위해 본 학위 논문에서는 메모리 채널 공유 아키텍처를 제안하였으며, 하드웨어 블록을 제안하였다. 이러한 채널 공유를 통해 멀티 프로그램 된 응용프로그램 및 다중 스레드 응용프로그램 성능이 각각 4.3 % 및 3.6 %로 향상되었으며 평균 읽기 대기 시간은 8.22 % 및 10.18 %로 감소하였다.Contents Abstract i Contents iv List of Figures vi List of Tables viii Introduction 1 1.1 3D-XPath: High-Density Managed DRAM Architecture with Cost-effective Alternative Paths for Memory Transactions 5 1.2 Boosting Bandwidth – Dynamic Channel Sharing on 3D Stacked Memory 9 1.3 Research contribution 13 1.4 Outline 14 3D-stacked Heterogeneous Memory Architecture with Cost-effective Extra Block Transfer Paths 17 2.1 Background 17 2.1.1 Heterogeneous Main Memory Systems 17 2.1.2 Specialized DRAM 19 2.1.3 3D-stacked Memory 22 2.2 HIGH-DENSITY DRAM ARCHITECTURE 27 2.2.1 Key Design Challenges 29 2.2.2 Plausible High-density DRAM Designs 33 2.3 3D-STACKED DRAM WITH ALTERNATIVE PATHS FOR MEMORY TRANSACTIONS 37 2.3.1 3D-XPath Architecture 41 2.3.2 3D-XPath Management 46 2.4 EXPERIMENTAL METHODOLOGY 52 2.5 EVALUATION 56 2.5.1 OLDI Workloads 56 2.5.2 Non-OLDI Workloads 61 2.5.3 Sensitivity Analysis 66 2.6 RELATED WORK 70 Boosting bandwidth –Dynamic Channel Sharing on 3D Stacked Memory 72 3.1 Background: Memory Operations 72 3.1.1. Memory Controller 72 3.1.2 DRAM column access sequence 73 3.2 Related Work 74 3.3. CHANNEL SHARING ENABLED MEMORY SYSTEM 76 3.3.1 Hardware Requirements 78 3.3.2 Operation Sequence 81 3.4 Analysis 87 3.4.1 Experiment Environment 87 3.4.2 Performance 88 3.4.3 Overhead 90 CONCLUSION 92 REFERENCES 94 국문초록 107Docto

Design Guidelines for High-Performance SCM Hierarchies

With emerging storage-class memory (SCM) nearing commercialization, there is evidence that it will deliver the much-anticipated high density and access latencies within only a few factors of DRAM. Nevertheless, the latency-sensitive nature of memory-resident services makes seamless integration of SCM in servers questionable. In this paper, we ask the question of how best to introduce SCM for such servers to improve overall performance/cost over existing DRAM-only architectures. We first show that even with the most optimistic latency projections for SCM, the higher memory access latency results in prohibitive performance degradation. However, we find that deployment of a modestly sized high-bandwidth 3D stacked DRAM cache makes the performance of an SCM-mostly memory system competitive. The high degree of spatial locality that memory-resident services exhibit not only simplifies the DRAM cache's design as page-based, but also enables the amortization of increased SCM access latencies and the mitigation of SCM's read/write latency disparity. We identify the set of memory hierarchy design parameters that plays a key role in the performance and cost of a memory system combining an SCM technology and a 3D stacked DRAM cache. We then introduce a methodology to drive provisioning for each of these design parameters under a target performance/cost goal. Finally, we use our methodology to derive concrete results for specific SCM technologies. With PCM as a case study, we show that a two bits/cell technology hits the performance/cost sweet spot, reducing the memory subsystem cost by 40% while keeping performance within 3% of the best performing DRAM-only system, whereas single-level and triple-level cell organizations are impractical for use as memory replacements.Comment: Published at MEMSYS'1

Principled Approaches to Last-Level Cache Management

Memory is a critical component of all computing systems. It represents a fundamental performance and energy bottleneck. Ideally, memory aspects such as energy cost, performance, and the cost of implementing management techniques would scale together with the size of all different computing systems; unfortunately this is not the case. With the upcoming trends in applications, new memory technologies, etc., scaling becomes a bigger a problem, aggravating the performance bottleneck that memory represents. A memory hierarchy was proposed to alleviate the problem. Each level in the hierarchy tends to have a decreasing cost per bit, an increased capacity, and a higher access time compared to its previous level. Preferably all data will be stored in the fastest level of memory, unfortunately, faster memory technologies tend to be associated with a higher manufacturing cost, which often limits their capacity. The design challenge is, to determine which is the frequently used data, and store it in the faster levels of memory. A cache is a small, fast, on-chip chunk of memory. Any data stored in main memory can be stored in the cache. For many programs, a typical behavior is to access data that has been accessed previously. Taking advantage of this behavior, a copy of frequently accessed data is kept in the cache, in order to provide a faster access time next time is requested. Due to capacity constrains, it is likely that all of the frequently reused data cannot fit in the cache, because of this, cache management policies decide which data is to be kept in the cache, and which in other levels of the memory hierarchy. Under an efficient cache management policy, an encouraging amount of memory requests will be serviced from a fast on-chip cache. The disparity in access latency between the last-level cache and main memory motivates the search for efficient cache management policies. There is a great amount of recently proposed work that strives to utilize cache capacity in the most favorable to performance way possible. Related work focus on optimizing the performance of caches focusing on different possible solutions, e.g. reduce miss rate, consume less power, reducing storage overhead, reduce access latency, etc. Our work focus on improving the performance of last-level caches by designing policies based on principles adapted from other areas of interest. In this dissertation, we focus on several aspects of cache management policies, we first introduce a space-efficient placement and promotion policy which goal is to minimize the updates to the replacement policy state on each cache access. We further introduce a mechanism that predicts whether a block in the cache will be reused, it feeds different features from a block to the predictor in order to increase the correlation of a previous access to a future access. We later introduce a technique that tweaks traditional cache indexing, providing fast accesses to a vast majority of requests in the presence of a slow access memory technology such as DRAM

HW-Flow: A Multi-Abstraction Level HW-CNN Codesign Pruning Methodology

Convolutional neural networks (CNNs) have produced unprecedented accuracy for many computer vision problems in the recent past. In power and compute-constrained embedded platforms, deploying modern CNNs can present many challenges. Most CNN architectures do not run in real-time due to the high number of computational operations involved during the inference phase. This emphasizes the role of CNN optimization techniques in early design space exploration. To estimate their efficacy in satisfying the target constraints, existing techniques are either hardware (HW) agnostic, pseudo-HW-aware by considering parameter and operation counts, or HW-aware through inflexible hardware-in-the-loop (HIL) setups. In this work, we introduce HW-Flow, a framework for optimizing and exploring CNN models based on three levels of hardware abstraction: Coarse, Mid and Fine. Through these levels, CNN design and optimization can be iteratively refined towards efficient execution on the target hardware platform. We present HW-Flow in the context of CNN pruning by augmenting a reinforcement learning agent with key metrics to understand the influence of its pruning actions on the inference hardware. With 2× reduction in energy and latency, we prune ResNet56, ResNet50, and DeepLabv3 with minimal accuracy degradation on the CIFAR-10, ImageNet, and CityScapes datasets, respectively