HAPPY: Hybrid Address-based Page Policy in DRAMs

Memory controllers have used static page closure policies to decide whether a row should be left open, open-page policy, or closed immediately, close-page policy, after the row has been accessed. The appropriate choice for a particular access can reduce the average memory latency. However, since application access patterns change at run time, static page policies cannot guarantee to deliver optimum execution time. Hybrid page policies have been investigated as a means of covering these dynamic scenarios and are now implemented in state-of-the-art processors. Hybrid page policies switch between open-page and close-page policies while the application is running, by monitoring the access pattern of row hits/conflicts and predicting future behavior. Unfortunately, as the size of DRAM memory increases, fine-grain tracking and analysis of memory access patterns does not remain practical. We propose a compact memory address-based encoding technique which can improve or maintain the performance of DRAMs page closure predictors while reducing the hardware overhead in comparison with state-of-the-art techniques. As a case study, we integrate our technique, HAPPY, with a state-of-the-art monitor, the Intel-adaptive open-page policy predictor employed by the Intel Xeon X5650, and a traditional Hybrid page policy. We evaluate them across 70 memory intensive workload mixes consisting of single-thread and multi-thread applications. The experimental results show that using the HAPPY encoding applied to the Intel-adaptive page closure policy can reduce the hardware overhead by 5X for the evaluated 64 GB memory (up to 40X for a 512 GB memory) while maintaining the prediction accuracy

DReAM: Dynamic Re-arrangement of Address Mapping to Improve the Performance of DRAMs

The initial location of data in DRAMs is determined and controlled by the 'address-mapping' and even modern memory controllers use a fixed and run-time-agnostic address mapping. On the other hand, the memory access pattern seen at the memory interface level will dynamically change at run-time. This dynamic nature of memory access pattern and the fixed behavior of address mapping process in DRAM controllers, implied by using a fixed address mapping scheme, means that DRAM performance cannot be exploited efficiently. DReAM is a novel hardware technique that can detect a workload-specific address mapping at run-time based on the application access pattern which improves the performance of DRAMs. The experimental results show that DReAM outperforms the best evaluated address mapping on average by 9%, for mapping-sensitive workloads, by 2% for mapping-insensitive workloads, and up to 28% across all the workloads. DReAM can be seen as an insurance policy capable of detecting which scenarios are not well served by the predefined address mapping

THE EFFECTS OF AGGRESSIVE OUT-OF-ORDER MECHANISMS ON THE MEMORY SUB-SYSTEM

Contrary to existing work that demonstrate significant improvements in performance with larger reorder buffers, the work presented in this dissertation shows that larger instruction windows do not necessarily provide the significant improvements in performance. By using detailed models of the DRAM system and the memory subsystem, we show that increasing out-of-order aggressiveness by increasing reorder buffer sizes beyond 128 entries no longer buys any improvement in processor performance. In fact we observe that it can actually degrade processor performance. Additionally, this dissertation demonstrates a non-intuitive problem associated with the out-of-order execution of memory instructions: the reordering of memory instructions can cause a degradation in the performance of the memory subsystem. Specifically, we show that increasing out-of-order aggressiveness in terms of reorder buffer sizes increases the frequency of replay traps and data cache misses. The presentation of this problem in itself is of utmost significance: the very mechanisms commonly used to improve performance are sources of performance degradation in the memory subsystem. We observe that while the negative effects of out-of-order execution existed for only a small fraction of the time with small reorder buffers, eliminating other sources of stalls by increasing out-of-order capability introduces these unexpected side effects in the memory subsystem to represent significant overhead. This reveals that one can not overlook rarely occurring events in the memory subsystem. To gain insight on the source of the problem, we attempt to measure the degree to which memory system performance relies on out-of-order execution. Using the network communication concept of windowing, we decided to change the load/store scheduling window independently of the ALU scheduling window. Our study revealed that memory instructions issued out-of-order are the primary reason for the increase in the frequency of replay traps. On the other hand, the out-of-order issue of memory instructions is responsible for the constructive and destructive references to the data cache. Incorporating detailed memory subsystem models and a realistic DRAM model into existing simulators and filtering out the destructive references from the total cache references can allow for aggressive out-of-order cores to reap the true benefits of out-of-order execution

Fairness-aware scheduling on single-ISA heterogeneous multi-cores

Single-ISA heterogeneous multi-cores consisting of small (e.g., in-order) and big (e.g., out-of-order) cores dramatically improve energy- and power-efficiency by scheduling workloads on the most appropriate core type. A significant body of recent work has focused on improving system throughput through scheduling. However, none of the prior work has looked into fairness. Yet, guaranteeing that all threads make equal progress on heterogeneous multi-cores is of utmost importance for both multi-threaded and multi-program workloads to improve performance and quality-of-service. Furthermore, modern operating systems affinitize workloads to cores (pinned scheduling) which dramatically affects fairness on heterogeneous multi-cores. In this paper, we propose fairness-aware scheduling for single-ISA heterogeneous multi-cores, and explore two flavors for doing so. Equal-time scheduling runs each thread or workload on each core type for an equal fraction of the time, whereas equal-progress scheduling strives at getting equal amounts of work done on each core type. Our experimental results demonstrate an average 14% (and up to 25%) performance improvement over pinned scheduling through fairness-aware scheduling for homogeneous multi-threaded workloads; equal-progress scheduling improves performance by 32% on average for heterogeneous multi-threaded workloads. Further, we report dramatic improvements in fairness over prior scheduling proposals for multi-program workloads, while achieving system throughput comparable to throughput-optimized scheduling, and an average 21% improvement in throughput over pinned scheduling

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%

In-Line Interrupt Handling and Lock-Up Free Translation Lookaside Buffers (TLBs)

The effects of the general-purpose precise interrupt mechanisms in use for the past few decades have received very little attention. When modern out-of-order processors handle interrupts precisely, they typically begin by flushing the pipeline to make the CPU available to execute handler instructions. In doing so, the CPU ends up flushing many instructions that have been brought in to the reorder buffer. In particular, these instructions may have reached a very deep stage in the pipeline—representing significant work that is wasted. In addition, an overhead of several cycles and wastage of energy (per exception detected) can be expected in refetching and reexecuting the instructions flushed. This paper concentrates on improving the performance of precisely handling software managed translation look-aside buffer (TLB) interrupts, one of the most frequently occurring interrupts. The paper presents a novel method of in-lining the interrupt handler within the reorder buffer. Since the first level interrupt-handlers of TLBs are usually small, they could potentially fit in the reorder buffer along with the user-level code already there. In doing so, the instructions that would otherwise be flushed from the pipe need not be refetched and reexecuted. Additionally, it allows for instructions independent of the exceptional instruction to continue to execute in parallel with the handler code. By in-lining the TLB interrupt handler, this provides lock-up free TLBs. This paper proposes the prepend and append schemes of in-lining the interrupt handler into the available reorder buffer space. The two schemes are implemented on a performance model of the Alpha 21264 processor built by Alpha designers at the Palo Alto Design Center (PADC), California. We compare the overhead and performance impact of handling TLB interrupts by the traditional scheme, the append in-lined scheme, and the prepend in-lined scheme. For small, medium, and large memory footprints, the overhead is quantified by comparing the number and pipeline state of instructions flushed, the energy savings, and the performance improvements. We find that lock-up free TLBs reduce the overhead of refetching and reexecuting the instructions flushed by 30-95 percent, reduce the execution time by 5-25 percent, and also reduce the energy wasted by 30-90 percent

Adaptive memory-side last-level GPU caching

Emerging GPU applications exhibit increasingly high computation demands which has led GPU manufacturers to build GPUs with an increasingly large number of streaming multiprocessors (SMs). Providing data to the SMs at high bandwidth puts significant pressure on the memory hierarchy and the Network-on-Chip (NoC). Current GPUs typically partition the memory-side last-level cache (LLC) in equally-sized slices that are shared by all SMs. Although a shared LLC typically results in a lower miss rate, we find that for workloads with high degrees of data sharing across SMs, a private LLC leads to a significant performance advantage because of increased bandwidth to replicated cache lines across different LLC slices. In this paper, we propose adaptive memory-side last-level GPU caching to boost performance for sharing-intensive workloads that need high bandwidth to read-only shared data. Adaptive caching leverages a lightweight performance model that balances increased LLC bandwidth against increased miss rate under private caching. In addition to improving performance for sharing-intensive workloads, adaptive caching also saves energy in a (co-designed) hierarchical two-stage crossbar NoC by power-gating and bypassing the second stage if the LLC is configured as a private cache. Our experimental results using 17 GPU workloads show that adaptive caching improves performance by 28.1% on average (up to 38.1%) compared to a shared LLC for sharing-intensive workloads. In addition, adaptive caching reduces NoC energy by 26.6% on average (up to 29.7%) and total system energy by 6.1% on average (up to 27.2%) when configured as a private cache. Finally, we demonstrate through a GPU NoC design space exploration that a hierarchical two-stage crossbar is both more power- and area-efficient than full and concentrated crossbars with the same bisection bandwidth, thus providing a low-cost cooperative solution to exploit workload sharing behavior in memory-side last-level caches

Fully-Buffered DIMM Memory Architectures: Understanding Mechanisms, Overheads and Scaling

Performance gains in memory have traditionally been obtained by increasing memory bus widths and speeds. The diminishing returns of such techniques have led to the proposal of an alternate architecture, the Fully-Buffered DIMM. This new standard replaces the conventional memory bus with a narrow, high-speed interface between the memory controller and the DIMMs. This paper examines how traditional DDRx based memory controller policies for scheduling and row buffer management perform on a Fully- Buffered DIMM memory architecture. The split-bus architecture used by FBDIMM systems results in an average improvement of 7% in latency and 10% in bandwidth at higher utilizations. On the other hand, at lower utilizations, the increased cost of serialization resulted in a degradation in latency and bandwidth of 25% and 10% respectively. The split-bus architecture also makes the system performance sensitive to the ratio of read and write traffic in the workload. In larger configurations, we found that the FBDIMM system performance was more sensitive to usage of the FBDIMM links than to DRAM bank availability. In general, FBDIMM performance is similar to that of DDRx systems, and provides better performance characteristics at higher utilization, making it a relatively inexpensive mechanism for scaling capacity at higher bandwidth requirements. The mechanism is also largely insensitive to scheduling policies, provided certain ground rules are obeyed

Automatic SMT threading for OpenMP applications on the Intel Xeon Phi co-processor

Simultaneous multithreading is a technique that can improve performance when running parallel applications on the Intel Xeon Phi co-processor. Selecting the most efficient thread count is however non-trivial, as the potential increase in efficiency has to be balanced against other, potentially negative factors such as inter-thread competition for cache capacity and increased synchronization overheads. In this paper, we extend CRUST (ClusteR-aware Under-subscribed Scheduling of Threads), a technique for finding the optimum thread count of OpenMP applications running on clustered cache architectures, to take the behavior of simultaneous multithreading on the Xeon Phi into account. CRUST can automatically find the optimum thread count at sub-application granularity by exploiting application phase behavior at OpenMP parallel section boundaries, and uses hardware performance counter information to gain insight into the application's behavior. We implement a CRUST prototype inside the Intel OpenMP runtime library and show its efficiency running on real Xeon Phi hardware

Beyond the socket: NUMA-aware GPUs

GPUs achieve high throughput and power efficiency by employing many small single instruction multiple thread (SIMT) cores. To minimize scheduling logic and performance variance they utilize a uniform memory system and leverage strong data parallelism exposed via the programming model. With Moore's law slowing, for GPUs to continue scaling performance (which largely depends on SIMT core count) they are likely to embrace multi-socket designs where transistors are more readily available. However when moving to such designs, maintaining the illusion of a uniform memory system is increasingly difficult. In this work we investigate multi-socket non-uniform memory access (NUMA) GPU designs and show that significant changes are needed to both the GPU interconnect and cache architectures to achieve performance scalability. We show that application phase effects can be exploited allowing GPU sockets to dynamically optimize their individual interconnect and cache policies, minimizing the impact of NUMA effects. Our NUMA-aware GPU outperforms a single GPU by 1.5×, 2.3×, and 3.2× while achieving 89%, 84%, and 76% of theoretical application scalability in 2, 4, and 8 sockets designs respectively. Implementable today, NUMA-aware multi-socket GPUs may be a promising candidate for scaling GPU performance beyond a single socket.We would like to thank anonymous reviewers and Steve Keckler for their help in improving this paper. The first author is supported by the Ministry of Economy and Competitiveness of Spain (TIN2012-34557, TIN2015-65316-P, and BES-2013-063925)Peer ReviewedPostprint (published version