Kevin Kai-Wei Chang

Staged memory scheduling: achieving high performance and scalability in heterogeneous systems

When multiple processor (CPU) cores and a GPU integrated together on the same chip share the off-chip main memory, requests from the GPU can heavily interfere with requests from the CPU cores, leading to low system performance and starvation of CPU cores. Unfortunately, state-of-the-art application-aware memory scheduling algorithms are ineffective at solving this problem at low complexity due to the large amount of GPU traffic. A large and costly request buffer is needed to provide these algorithms with enough visibility across the global request stream, requiring relatively complex hardware implementations. This paper proposes a fundamentally new approach that decouples the memory controllers three primary tasks into three significantly simpler structures that together improve system performance and fairness, especially in integrated CPU-GPU systems. Our three-stage memory controller first groups requests based on row-buffer locality. This grouping allows the second stage to focus only on inter-application request scheduling. These two stages enforce high-level policies regarding performance and fairness, and therefore the last stage consists of simple per-bank FIFO queues (no further command reordering within each bank) and straightforward logic that deals only with low-level DRAM commands and timing. We evaluate the design trade-offs involved in our Staged Memory Scheduler (SMS) and compare it against three state-of-the-art memory controller designs. Our evaluations show that SMS improves CPU performance without degrading GPU frame rate beyond a generally acceptable level, while being significantly less complex to implement than previous application-aware schedulers. Furthermore, SMS can be configured by the system software to prioritize the CPU or the GPU at varying levels to address different performance needs.

Improving DRAM performance by parallelizing refreshes with accesses

Modern DRAM cells are periodically refreshed to prevent data loss due to leakage. Commodity DDR (double data rate) DRAM refreshes cells at the rank level. This degrades performance significantly because it prevents an entire DRAM rank from serving memory requests while being refreshed. DRAM designed for mobile platforms, LPDDR (low power DDR) DRAM, supports an enhanced mode, called per-bank refresh, that refreshes cells at the bank level. This enables a bank to be accessed while another in the same rank is being refreshed, alleviating part of the negative performance impact of refreshes. Unfortunately, there are two shortcomings of per-bank refresh employed in todays systems. First, we observe that the perbank refresh scheduling scheme does not exploit the full potential of overlapping refreshes with accesses across banks because it restricts the banks to be refreshed in a sequential round-robin order. Second, accesses to a bank that is being refreshed have to wait. To mitigate the negative performance impact of DRAM refresh, we propose two complementary mechanisms, DARP (Dynamic Access Refresh Parallelization) and SARP (Subarray Access Refresh Parallelization). The goal is to address the drawbacks of per-bank refresh by building more efficient techniques to parallelize refreshes and accesses within DRAM. First, instead of issuing per-bank refreshes in a round-robin order, as it is done today, DARP issues per-bank refreshes to idle banks in an out-of-order manner. Furthermore, DARP proactively schedules refreshes during intervals when a batch of writes are draining to DRAM. Second, SARP exploits the existence of mostly-independent subarrays within a bank. With minor modifications to DRAM organization, it allows a bank to serve memory accesses to an idle subarray while another subarray is being refreshed. Extensive evaluations on a wide variety of workloads and systems show that our mechanisms improve system performance (and energy efficiency) compared to three state-of-the-art refresh policies and the performance benefit increases as DRAM density increases.

Adaptive-latency DRAM: Optimizing DRAM timing for the common-case

In current systems, memory accesses to a DRAM chip must obey a set of minimum latency restrictions specified in the DRAM standard. Such timing parameters exist to guarantee reliable operation. When deciding the timing parameters, DRAM manufacturers incorporate a very large margin as a provision against two worst-case scenarios. First, due to process variation, some outlier chips are much slower than others and cannot be operated as fast. Second, chips become slower at higher temperatures, and all chips need to operate reliably at the highest supported (i.e., worst-case) DRAM temperature (85° C). In this paper, we show that typical DRAM chips operating at typical temperatures (e.g., 55° C) are capable of providing a much smaller access latency, but are nevertheless forced to operate at the largest latency of the worst-case. Our goal in this paper is to exploit the extra margin that is built into the DRAM timing parameters to improve performance. Using an FPGA-based testing platform, we first characterize the extra margin for 115 DRAM modules from three major manufacturers. Our results demonstrate that it is possible to reduce four of the most critical timing parameters by a minimum/maximum of 17.3%/54.8% at 55°C without sacrificing correctness. Based on this characterization, we propose Adaptive-Latency DRAM (AL-DRAM), a mechanism that adoptively reduces the timing parameters for DRAM modules based on the current operating condition. AL-DRAM does not require any changes to the DRAM chip or its interface. We evaluate AL-DRAM on a real system that allows us to reconfigure the timing parameters at runtime. We show that AL-DRAM improves the performance of memory-intensive workloads by an average of 14% without introducing any errors. We discuss and show why AL-DRAM does not compromise reliability. We conclude that dynamically optimizing the DRAM timing parameters can reliably improve system performance.

Low-Cost Inter-Linked Subarrays (LISA): Enabling fast inter-subarray data movement in DRAM

This paper introduces a new DRAM design that enables fast and energy-efficient bulk data movement across subarrays in a DRAM chip. While bulk data movement is a key operation in many applications and operating systems, contemporary systems perform this movement inefficiently, by transferring data from DRAM to the processor, and then back to DRAM, across a narrow off-chip channel. The use of this narrow channel for bulk data movement results in high latency and energy consumption. Prior work proposed to avoid these high costs by exploiting the existing wide internal DRAM bandwidth for bulk data movement, but the limited connectivity of wires within DRAM allows fast data movement within only a single DRAM subarray. Each subarray is only a few megabytes in size, greatly restricting the range over which fast bulk data movement can happen within DRAM. We propose a new DRAM substrate, Low-Cost Inter-Linked Subarrays (LISA), whose goal is to enable fast and efficient data movement across a large range of memory at low cost. LISA adds low-cost connections between adjacent subarrays. By using these connections to interconnect the existing internal wires (bitlines) of adjacent subarrays, LISA enables wide-bandwidth data transfer across multiple subarrays with little (only 0.8%) DRAM area overhead. As a DRAM substrate, LISA is versatile, enabling an array of new applications. We describe and evaluate three such applications in detail: (1) fast inter-subarray bulk data copy, (2) in-DRAM caching using a DRAM architecture whose rows have heterogeneous access latencies, and (3) accelerated bitline precharging by linking multiple precharge units together. Our extensive evaluations show that each of LISAs three applications significantly improves performance and memory energy efficiency, and their combined benefit is higher than the benefit of each alone, on a variety of workloads and system configurations.

DASH: Deadline-Aware High-Performance Memory Scheduler for Heterogeneous Systems with Hardware Accelerators

Modern SoCs integrate multiple CPU cores and hardware accelerators (HWAs) that share the same main memory system, causing interference among memory requests from different agents. The result of this interference, if it is not controlled well, is missed deadlines for HWAs and low CPU performance. Few previous works have tackled this problem. State-of-the-art mechanisms designed for CPU-GPU systems strive to meet a target frame rate for GPUs by prioritizing the GPU close to the time when it has to complete a frame. We observe two major problems when such an approach is adapted to a heterogeneous CPU-HWA system. First, HWAs miss deadlines because they are prioritized only when close to their deadlines. Second, such an approach does not consider the diverse memory access characteristics of different applications running on CPUs and HWAs, leading to low performance for latency-sensitive CPU applications and deadline misses for some HWAs, including GPUs. In this article, we propose a Deadline-Aware memory Scheduler for Heterogeneous systems (DASH), which overcomes these problems using three key ideas, with the goal of meeting HWAs’ deadlines while providing high CPU performance. First, DASH prioritizes an HWA when it is not on track to meet its deadline any time during a deadline period, instead of prioritizing it only when close to a deadline. Second, DASH prioritizes HWAs over memory-intensive CPU applications based on the observation that memory-intensive applications’ performance is not sensitive to memory latency. Third, DASH treats short-deadline HWAs differently as they are more likely to miss their deadlines and schedules their requests based on worst-case memory access time estimates. Extensive evaluations across a wide variety of different workloads and systems show that DASH achieves significantly better CPU performance than the best previous scheduler while always meeting the deadlines for all HWAs, including GPUs, thereby largely improving frame rates.

Design and Evaluation of Hierarchical Rings with Deflection Routing

Hierarchical ring networks, which hierarchically connect multiple levels of rings, have been proposed in the past to improve the scalability of ring interconnects, but past hierarchical ring designs sacrifice some of the key benefits of rings by reintroducing more complex in-ring buffering and buffered flow control. Our goal in this paper is to design a new hierarchical ring interconnect that can maintain most of the simplicity of traditional ring designs (i.e., no in-ring buffering or buffered flow control) while achieving high scalability as more complex buffered hierarchical ring designs. To this end, we revisit the concept of a hierarchical-ring networkon-chip. Our design, called HiRD (Hierarchical Rings with Deflection), includes critical features that enable us to mostly maintain the simplicity of traditional simple ring topologies while providing higher energy efficiency and scalability. First, HiRD does not have any buffering or buffered flow control within individual rings, and requires only a small amount of buffering between the ring hierarchy levels. When inter-ring buffers are full, our design simply deflects flits so that they circle the ring and try again, which eliminates the need for in-ring buffering. Second, we introduce two simple mechanisms that together provide an end-to-end delivery guarantee within the entire network (despite any deflections that occur) without impacting the critical path or latency of the vast majority of network traffic. Our experimental evaluations on a wide variety of multiprogrammed and multithreaded workloads and synthetic traffic patterns show that HiRD attains equal or better performance at better energy efficiency than multiple versions of both a previous hierarchical ring design and a traditional single ring design. We also extensively analyze our designs characteristics and injection and delivery guarantees. We conclude that HiRD can be a compelling design point that allows higher energy efficiency and scalability while retaining the simplicity and appeal of conventional ring-based designs.

Understanding Reduced-Voltage Operation in Modern DRAM Devices: Experimental Characterization, Analysis, and Mechanisms

The energy consumption of DRAM is a critical concern in modern computing systems. Improvements in manufacturing process technology have allowed DRAM vendors to lower the DRAM supply voltage conservatively, which reduces some of the DRAM energy consumption. We would like to reduce the DRAM supply voltage more aggressively, to further reduce energy. Aggressive supply voltage reduction requires a thorough understanding of the effect voltage scaling has on DRAM access latency and DRAM reliability. In this paper, we take a comprehensive approach to understanding and exploiting the latency and reliability characteristics of modern DRAM when the supply voltage is lowered below the nominal voltage level specified by manufacturers.

SoftMC: A Flexible and Practical Open-Source Infrastructure for Enabling Experimental DRAM Studies

DRAM is the primary technology used for main memory in modern systems. Unfortunately, as DRAM scales down to smaller technology nodes, it faces key challenges in both data integrity and latency, which strongly affects overall system reliability and performance. To develop reliable and high-performance DRAM-based main memory in future systems, it is critical to characterize, understand, and analyze various aspects (e.g., reliability, latency) of existing DRAM chips. To enable this, there is a strong need for a publicly-available DRAM testing infrastructure that can flexibly and efficiently test DRAM chips in a manner accessible to both software and hardware developers. This paper develops the first such infrastructure, SoftMC (Soft Memory Controller), an FPGA-based testing platform that can control and test memory modules designed for the commonly-used DDR (Double Data Rate) interface. SoftMC has two key properties: (i) it provides flexibility to thoroughly control memory behavior or to implement a wide range of mechanisms using DDR commands, and (ii) it is easy to use as it provides a simple and intuitive high-level programming interface for users, completely hiding the low-level details of the FPGA. We demonstrate the capability, flexibility, and programming ease of SoftMC with two example use cases. First, we implement a test that characterizes the retention time of DRAM cells. Experimental results we obtain using SoftMC are consistent with the findings of prior studies on retention time in modern DRAM, which serves as a validation of our infrastructure. Second, we validate two recently-proposed mechanisms, which rely on accessing recently-refreshed or recently-accessed DRAM cells faster than other DRAM cells. Using our infrastructure, we show that the expected latency reduction effect of these mechanisms is not observable in existing DRAM chips, which demonstrates the usefulness of SoftMC in testing new ideas on existing memory modules. We discuss several other use cases of SoftMC, including the ability to characterize emerging non-volatile memory modules that obey the DDR standard. We hope that our open-source release of SoftMC fills a gap in the space of publicly-available experimental memory testing infrastructures and inspires new studies, ideas, and methodologies in memory system design.

Accelerating pointer chasing in 3D-stacked memory: Challenges, mechanisms, evaluation

Pointer chasing is a fundamental operation, used by many important data-intensive applications (e.g., databases, key-value stores, graph processing workloads) to traverse linked data structures. This operation is both memory bound and latency sensitive, as it (1) exhibits irregular access patterns that cause frequent cache and TLB misses, and (2) requires the data from every memory access to be sent back to the CPU to determine the next pointer to access. Our goal is to accelerate pointer chasing by performing it inside main memory, thereby avoiding inefficient and high-latency data transfers between main memory and the CPU. To this end, we propose the In-Memory PoInter Chasing Accelerator (IMPICA), which leverages the logic layer within 3D-stacked memory for linked data structure traversal. This paper identifies the key design challenges of designing a pointer chasing accelerator in memory, describes new mechanisms employed within IMPICA to solve these challenges, and evaluates the performance and energy benefits of our accelerator. IMPICA addresses the key challenges of (1) how to achieve high parallelism in the presence of serial accesses in pointer chasing, and (2) how to effectively perform virtual-to-physical address translation on the memory side without requiring expensive accesses to the CPUs memory management unit. We show that the solutions to these challenges, address-access decoupling and a region-based page table, respectively, are simple and low-cost. We believe these solutions are also applicable to many other in-memory accelerators, which are likely to also face the two challenges. Our evaluations on a quad-core system show that IMPICA improves the performance of pointer chasing operations in three commonly-used linked data structures (linked lists, hash tables, and B-trees) by 92%, 29%, and 18%, respectively. This leads to a significant performance improvement in applications that utilize linked data structures - on a real database application, DBx1000, IMPICA improves transaction throughput and response time by 16% and 13%, respectively. IMPICA also significantly reduces overall system energy consumption (by 41%, 23%, and 10% for the three commonly-used data structures, and by 6% for DBx1000).

A case for hierarchical rings with deflection routing

This paper is the first to introduce a scalable and energy-efficient hierarchical ring design that relies on deflection routing and guarantees deadlock- and livelock-free packet delivery.We identify key drawbacks of previous hierarchical ring network designs with respect to scalability, performance and energy efficiency.We propose a new hierarchical ring network design that has simple routers with minimal buffering using deflection routing.We provide a new mechanism for guaranteed delivery of traffic in this network.Compared to 5 other alternative designs, we show that our hierarchical ring network design with deflection routing provides both higher energy-efficiency and higher system performance across a wide variety of workloads. Hierarchical ring networks, which hierarchically connect multiple levels of rings, have been proposed in the past to improve the scalability of ring interconnects, but past hierarchical ring designs sacrifice some of the key benefits of rings by reintroducing more complex in-ring buffering and buffered flow control. Our goal in this paper is to design a new hierarchical ring interconnect that can maintain most of the simplicity of traditional ring designs (i.e., no in-ring buffering or buffered flow control) while achieving high scalability as more complex buffered hierarchical ring designs.To this end, we revisit the concept of a hierarchical-ring network-on-chip. Our design, called HiRD (Hierarchical Rings with Deflection), includes critical features that enable us to mostly maintain the simplicity of traditional simple ring topologies while providing higher energy efficiency and scalability. First, HiRD does not have any buffering or buffered flow control within individual rings, and requires only a small amount of buffering between the ring hierarchy levels. When inter-ring buffers are full, our d7sesign simply deflects flits so that they circle the ring and try again, which eliminates the need for in-ring buffering. Second, we introduce two simple mechanisms that together provide an end-to-end delivery guarantee within the entire network (despite any deflections that occur) without impacting the critical path or latency of the vast majority of network traffic.Our experimental evaluations on a wide variety of multiprogrammed and multithreaded workloads and synthetic traffic patterns show that HiRD attains equal or better performance at better energy efficiency than multiple versions of both a previous hierarchical ring design and a traditional single ring design. We also extensively analyze our designs characteristics and injection and delivery guarantees. We conclude that HiRD can be a compelling design point that allows higher energy efficiency and scalability while retaining the simplicity and appeal of conventional ring-based designs.