Justin Meza

Bio: Justin Meza is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: Dram & Cache. The author has an hindex of 20, co-authored 37 publications receiving 2096 citations. Previous affiliations of Justin Meza include University of California, Los Angeles & Facebook.

Row buffer locality aware caching policies for hybrid memories

Abstract: Phase change memory (PCM) is a promising technology that can offer higher capacity than DRAM. Unfortunately, PCM's access latency and energy are higher than DRAM's and its endurance is lower. Many DRAM-PCM hybrid memory systems use DRAM as a cache to PCM, to achieve the low access latency and energy, and high endurance of DRAM, while taking advantage of PCM's large capacity. A key question is what data to cache in DRAM to best exploit the advantages of each technology while avoiding its disadvantages as much as possible. We propose a new caching policy that improves hybrid memory performance and energy efficiency. Our observation is that both DRAM and PCM banks employ row buffers that act as a cache for the most recently accessed memory row. Accesses that are row buffer hits incur similar latencies (and energy consumption) in DRAM and PCM, whereas accesses that are row buffer misses incur longer latencies (and higher energy consumption) in PCM. To exploit this, we devise a policy that avoids accessing in PCM data that frequently causes row buffer misses because such accesses are costly in terms of both latency and energy. Our policy tracks the row buffer miss counts of recently used rows in PCM, and caches in DRAM the rows that are predicted to incur frequent row buffer misses. Our proposed caching policy also takes into account the high write latencies of PCM, in addition to row buffer locality. Compared to a conventional DRAM-PCM hybrid memory system, our row buffer locality-aware caching policy improves system performance by 14% and energy efficiency by 10% on data-intensive server and cloud-type workloads. The proposed policy achieves 31% performance gain over an all-PCM memory system, and comes within 29% of the performance of an allDRAM memory system (not taking PCM's capacity benefit into account) on evaluated workloads.

A Large-Scale Study of Flash Memory Failures in the Field

Abstract: Servers use flash memory based solid state drives (SSDs) as a high-performance alternative to hard disk drives to store persistent data. Unfortunately, recent increases in flash density have also brought about decreases in chip-level reliability. In a data center environment, flash-based SSD failures can lead to downtime and, in the worst case, data loss. As a result, it is important to understand flash memory reliability characteristics over flash lifetime in a realistic production data center environment running modern applications and system software. This paper presents the first large-scale study of flash-based SSD reliability in the field. We analyze data collected across a majority of flash-based solid state drives at Facebook data centers over nearly four years and many millions of operational hours in order to understand failure properties and trends of flash-based SSDs. Our study considers a variety of SSD characteristics, including: the amount of data written to and read from flash chips; how data is mapped within the SSD address space; the amount of data copied, erased, and discarded by the flash controller; and flash board temperature and bus power. Based on our field analysis of how flash memory errors manifest when running modern workloads on modern SSDs, this paper is the first to make several major observations: (1) SSD failure rates do not increase monotonically with flash chip wear; instead they go through several distinct periods corresponding to how failures emerge and are subsequently detected, (2) the effects of read disturbance errors are not prevalent in the field, (3) sparse logical data layout across an SSD's physical address space (e.g., non-contiguous data), as measured by the amount of metadata required to track logical address translations stored in an SSD-internal DRAM buffer, can greatly affect SSD failure rate, (4) higher temperatures lead to higher failure rates, but techniques that throttle SSD operation appear to greatly reduce the negative reliability impact of higher temperatures, and (5) data written by the operating system to flash-based SSDs does not always accurately indicate the amount of wear induced on flash cells due to optimizations in the SSD controller and buffering employed in the system software. We hope that the findings of this first large-scale flash memory reliability study can inspire others to develop other publicly-available analyses and novel flash reliability solutions.

Revisiting Memory Errors in Large-Scale Production Data Centers: Analysis and Modeling of New Trends from the Field

Abstract: Computing systems use dynamic random-access memory (DRAM) as main memory. As prior works have shown, failures in DRAM devices are an important source of errors in modern servers. To reduce the effects of memory errors, error correcting codes (ECC) have been developed to help detect and correct errors when they occur. In order to develop effective techniques, including new ECC mechanisms, to combat memory errors, it is important to understand the memory reliability trends in modern systems. In this paper, we analyze the memory errors in the entire fleet of servers at Facebook over the course of fourteen months, representing billions of device days. The systems we examine cover a wide range of devices commonly used in modern servers, with DIMMs manufactured by 4 vendors in capacities ranging from 2 GB to 24 GB that use the modern DDR3 communication protocol. We observe several new reliability trends for memory systems that have not been discussed before in literature. We show that (1) memory errors follow a power-law, specifically, a Pareto distribution with decreasing hazard rate, with average error rate exceeding median error rate by around 55×, (2) non-DRAM memory failures from the memory controller and memory channel cause the majority of errors, and the hardware and software overheads to handle such errors cause a kind of denial of service attack in some servers, (3) using our detailed analysis, we provide the first evidence that more recent DRAM cell fabrication technologies (as indicated by chip density) have substantially higher failure rates, increasing by 1.8× over the previous generation, (4) DIMM architecture decisions affect memory reliability: DIMMs with fewer chips and lower transfer widths have the lowest error rates, likely due to electrical noise reduction, (5) while CPU and memory utilization do not show clear trends with respect to failure rates, workload type can influence failure rate by up to 6:5×, suggesting certain memory access patterns may induce more errors, (6) we develop a model for memory reliability and show how system design choices such as using lower density DIMMs and fewer cores per chip can reduce failure rates of a baseline server by up to 57.7%, and (7) we perform the first implementation and real-system analysis of page offlining at scale, showing that it can reduce memory error rate by 67%, and identify several real-world impediments to the technique.

Gorilla: a fast, scalable, in-memory time series database

Abstract: Large-scale internet services aim to remain highly available and responsive in the presence of unexpected failures. Providing this service often requires monitoring and analyzing tens of millions of measurements per second across a large number of systems, and one particularly effective solution is to store and query such measurements in a time series database (TSDB).A key challenge in the design of TSDBs is how to strike the right balance between efficiency, scalability, and reliability. In this paper we introduce Gorilla, Facebook's in-memory TSDB. Our insight is that users of monitoring systems do not place much emphasis on individual data points but rather on aggregate analysis, and recent data points are of much higher value than older points to quickly detect and diagnose the root cause of an ongoing problem. Gorilla optimizes for remaining highly available for writes and reads, even in the face of failures, at the expense of possibly dropping small amounts of data on the write path. To improve query efficiency, we aggressively leverage compression techniques such as delta-of-delta timestamps and XOR'd floating point values to reduce Gorilla's storage footprint by 10x. This allows us to store Gorilla's data in memory, reducing query latency by 73x and improving query throughput by 14x when compared to a traditional database (HBase)-backed time series data. This performance improvement has unlocked new monitoring and debugging tools, such as time series correlation search and more dense visualization tools. Gorilla also gracefully handles failures from a single-node to entire regions with little to no operational overhead.

Enabling Efficient and Scalable Hybrid Memories Using Fine-Granularity DRAM Cache Management

Abstract: Hybrid main memories composed of DRAM as a cache to scalable non-volatile memories such as phase-change memory (PCM) can provide much larger storage capacity than traditional main memories. A key challenge for enabling high-performance and scalable hybrid memories, though, is efficiently managing the metadata (e.g., tags) for data cached in DRAM at a fine granularity. Based on the observation that storing metadata off-chip in the same row as their data exploits DRAM row buffer locality, this paper reduces the overhead of fine-granularity DRAM caches by only caching the metadata for recently accessed rows on-chip using a small buffer. Leveraging the flexibility and efficiency of such a fine-granularity DRAM cache, we also develop an adaptive policy to choose the best granularity when migrating data into DRAM. On a hybrid memory with a 512MB DRAM cache, our proposal using an 8KB on-chip buffer can achieve within 6% of the performance of, and 18% better energy efficiency than, a conventional 8MB SRAM metadata store, even when the energy overhead due to large SRAM metadata storage is not considered.

The Act of Creation

Abstract: While the study of psychology has offered little in the way of explaining the creative process, Koestler examines the idea that we are at our most creative when rational thought is suspended--for example, in dreams and trancelike states. All who read The Act of Creation will find it a compelling and illuminating book.

The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines

Abstract: As computation continues to move into the cloud, the computing platform of interest no longer resembles a pizza box or a refrigerator, but a warehouse full of computers. These new large datacenters are quite different from traditional hosting facilities of earlier times and cannot be viewed simply as a collection of co-located servers. Large portions of the hardware and software resources in these facilities must work in concert to efficiently deliver good levels of Internet service performance, something that can only be achieved by a holistic approach to their design and deployment. In other words, we must treat the datacenter itself as one massive warehouse-scale computer (WSC). We describe the architecture of WSCs, the main factors influencing their design, operation, and cost structure, and the characteristics of their software base. We hope it will be useful to architects and programmers of today's WSCs, as well as those of future many-core platforms which may one day implement the equivalent of today's WSCs on a single board. Table of Contents: Introduction / Workloads and Software Infrastructure / Hardware Building Blocks / Datacenter Basics / Energy and Power Efficiency / Modeling Costs / Dealing with Failures and Repairs / Closing Remarks

Computer Architecture, Fifth Edition: A Quantitative Approach

Abstract: The computing world today is in the middle of a revolution: mobile clients and cloud computing have emerged as the dominant paradigms driving programming and hardware innovation today. The Fifth Edition of Computer Architecture focuses on this dramatic shift, exploring the ways in which software and technology in the "cloud" are accessed by cell phones, tablets, laptops, and other mobile computing devices. Each chapter includes two real-world examples, one mobile and one datacenter, to illustrate this revolutionary change. Updated to cover the mobile computing revolutionEmphasizes the two most important topics in architecture today: memory hierarchy and parallelism in all its forms.Develops common themes throughout each chapter: power, performance, cost, dependability, protection, programming models, and emerging trends ("What's Next")Includes three review appendices in the printed text. Additional reference appendices are available online.Includes updated Case Studies and completely new exercises.