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Multi-Level Cell Phase Change Memory (MLC PCM) is a promising candidate technology for DRAM replacement in main memory of modern computers. Despite of its high density and low power advantages, this technology seriously suffers from slow read and write operations. While prior works extensively studied the problem of slow write, this paper targets high read latency problem in MLC PCM and introduces an architecture mechanism to overcome it. To this end, we rely on the fact that reading different bits from an MLC cell takes different latencies, i.e., for a 2-bit MLC, reading its Most-Significant Bit (MSB) is fast, while reading its Least-Significant Bits (LSBs) is slower. We then propose Half-Line PCM (HL-PCM), a novel memory architecture that leverages this non-uniformity in reading MLC PCM’s content to send a requested memory block to the processor in different cycles–it sends half of a memory block to the processor ahead of the other half. If the processor requested a word belonging to the first half, it can resume its execution on receiving the first half, while the other half has not sent yet and scheduled to be received by the memory controller later. HL-PCM is easy and simple to implement, i.e., it needs minor modifications at memory controller, the search/evict policies at last level cache, as well as data layout in main memory. Our experimental results show that the proposed design improves the average memory access latency by 33–43 percent and program’s execution time by 23 percent, on average, while incurring negligible overhead at memory controller and PCM DIMM, in a 16-core chip multiprocessor (CMP) running memory-intensive benchmarks. [ABSTRACT FROM PUBLISHER]

File layout of array data is a critical factor that effects the behavior of storage caches, and has so far taken not much attention in the context of hierarchical storage systems. The main contribution of this paper is a compiler-driven file layout optimization scheme for hierarchical storage caches. This approach, fully automated within an optimizing compiler, analyzes a multi-threaded application code and determines a file layout for each disk-resident array referenced by the code, such that the performance of the target storage cache hierarchy is maximized. We tested our approach using 16 I/O intensive application programs and compared its performance against two previously proposed approaches under different cache space management schemes. Our experimental results show that the proposed approach improves the execution time of these parallel applications by 23.7% on average. [ABSTRACT FROM AUTHOR]

Fabricating circuits that employ ever-smaller transistors leads to dramatic variations in critical process parameters. This in turn results in large variations in execution/access latencies of different hardware components, This situation is even more severe for memory components due to minimum-sized transistors used in their design. Current design methodologies that are tuned for the worst case scenarios are becoming increasingly pessimistic from the performance angle, and thus, may not be a viable option at all for future designs. This paper makes two contributions targeting on-chip data caches. First, it presents an adaptive cache management policy based on nonuniform cache access, Second, it proposes a latency compensation approach that employs several circuit-level techniques to change the access latency of select cache lines based on the criticalities of the load instructions that access them. Our experiments reveal that both these techniques can recover significant amount of the lost performance due to worst case designs. [ABSTRACT FROM AUTHOR]

Disk subsystem is known to be a major contributor to overall power consumption of high-end parallel systems. Past research proposed several architectural-level techniques to reduce disk power by taking advantage of idle periods experienced by disks. Although such techniques have been known to be effective in certain cases, they share a common drawback: They operate in a reactive manner, i.e., they control disk power by observing past disk activity (for example, idle and active periods) and estimating future ones. Consequently, they can miss opportunities for saving power and incur significant performance penalties due to inaccuracies in predicting idle and active times. Motivated by this observation, this paper proposes and evaluates a compiler-driven approach to reducing disk power consumption of array-based scientific applications executing on parallel architectures. The proposed approach exposes disk layout information to the compiler, allowing it to derive the disk access pattern, i.e., the order in which parallel disks are accessed. This paper demonstrates two uses of this information. First, we can implement proactive disk power management, i.e., we can select the most appropriate power-saving strategy and disk-preactivation strategy based on the compiler-predicted future idle and active periods of parallel disks. Second, we can restructure the application code to increase the length of idle disk periods, which leads to better exploitation of available power-saving capabilities. We implemented both these approaches within an optimizing compiler and tested their effectiveness using a set of benchmark codes from the Spec 2000 suite and a disk power simulator. Our results show that the compiler-driven disk power management is very promising. The experimental results also reveal that, although proactive disk power management is very effective, code restructuring for disk power achieves additional energy savings across all the benchmarks tested, and these savings are very close to optimal savings that can be obtained through an Integer Linear Programming (ILP)-based scheme. [ABSTRACT FROM AUTHOR]

One of the key challenges facing computer architects and compiler writers is the increasing discrepancy between processor cycle times and main memory access times. To alleviate this problem in array-intensive embedded signal and video processing applications, compilers may employ either control-centric transformations that change data access patterns of nested loops or data-centric transformations that modify memory layouts of multidimensional arrays. Most of the memory layout optimizations proposed so far either modify the layout of each array independently or are based on explicit data reorganizations at runtime. This paper focuses on a compiler technique, called array regrouping, that automatically maps multiple arrays into a single data (array) space to improve data access pattern. We present a mathematical framework that enables us to systematically derive suitable mappings for a given array-intensive embedded application. The framework divides the arrays accessed in a given program into several groups and each group is independently layout-transformed to improve spatial locality and reduce the number of conflict misses. As compared to the previous approaches, the proposed technique makes two new contributions: 1) It presents a graph based formulation of the array regrouping problem and 2) it demonstrates potential benefits of this aggressive array-regrouping strategy in optimizing behavior of embedded systems. Extensive experimental results demonstrate significant improvements in cache miss rates and execution times. An important advantage of this approach over the previous techniques that target conflict misses is that it reduces conflict misses without increasing the data space requirements of the application being optimized. This is a very desirable property in many embedded/portable environments where data space requirements determine the minimum physical memory capacity. [ABSTRACT FROM AUTHOR]

Presents a study which investigated software and hardware techniques to take advantage of the dynamic random access memory (DRAM) mode control capabilities. Memory model for energy optimizations; Description of the compiler-directed approach to energy management for DRAM; Automatic insertion of mode instructions.

Examines a linear algebra framework for automatic determination of optimal data layouts. Expression of memory layouts by hyperplanes; Determination of data transformation matrices to implement optimal layouts; Programs on two distributed shared-memory multiprocessors.

Many large scale applications have significant I/O requirements as well as computational and memory requirements. Unfortunately, the limited number of I/O nodes provided in a typical configuration of the modern message-passing distributed-memory architectures such as Intel Paragon and IBM SP-2 limits the I/O performance of these applications severely. In this paper, we examine some software optimization techniques and evaluate their effects in five different I/O-intensive codes from both small and large application domains. Our goals in this study are twofold. First, we want to understand the behavior of large-scale data-intensive applications and the impact of I/O subsystems on their performance and vice versa. Second, and more importantly, we strive to determine the solutions for improving the applications' performance by a mix of software techniques. Our results reveal that different applications can benefit from different optimizations. For example, we found that some applications benefit from file layout optimizations whereas others take advantage of collective I/O. A combination of architectural and software solutions is normally needed to obtain good I/O performance. For example, we show that with a limited number of I/O resources, it is possible to obtain good performance by using appropriate software optimizations. We also show that beyond a certain level, imbalance in the architecture results in performance degradation even when using optimized software, thereby indicating the necessity of an increase in I/O resources. [ABSTRACT FROM AUTHOR]