Power dissipation is increasingly important in CPUs ranging from those intended for mobile use, all the way up to high-performance processors for high-end servers. While the bulk of the power dissipated is dynamic switching power, leakage power is also beginning to be a concern. Chipmakers expect that in future chip generations, leakage's proportion of total chip power will increase significantly.This paper examines methods for reducing leakage power within the cache memories of the CPU. Because caches comprise much of a CPU chip's area and transistor counts, they are reasonable targets for attacking leakage. We discuss policies and implementations for reducing cache leakage by invalidating and “turning off” cache lines when they hold data not likely to be reused. In particular, our approach is targeted at the generational nature of cache line usage. That is, cache lines typically have a flurry of frequent use when first brought into the cache, and then have a period of “dead time” before they are evicted. By devising effective, low-power ways of deducing dead time, our results show that in many cases we can reduce LI cache leakage energy by 4x in SPEC2000 applications without impacting performance. Because our decay-based techniques have notions of competitive on-line algorithms at their roots, their energy usage can be theoretically bounded at within a factor of two of the optimal oracle-based policy. We also examine adaptive decay-based policies that make energy-minimizing policy choices on a per-application basis by choosing appropriate decay intervals individually for each cache line. Our proposed adaptive policies effectively reduce LI cache leakage energy by 5x for the SPEC2000 with only negligible degradations in performance.

/pdf/cache-decay-exploiting-generational-behavior-to-reduce-cache-3wl24vywlv.pdf

Cache decay: exploiting generational behavior to reduce cache leakage power

This thesis addresses the challenges of building a software system for general-purpose runtime code manipulation. Modern applications, with dynamically-loaded modules and dynamically-generated code, are assembled at runtime. While it was once feasible at compile time to observe and manipulate every instruction—which is critical for program analysis, instrumentation, trace gathering, optimization, and similar tools—it can now only be done at runtime. Existing runtime tools are successful at inserting instrumentation calls, but no general framework has been developed for fine-grained and comprehensive code observation and modification without high overheads. 
This thesis demonstrates the feasibility of building such a system in software. We present DynamoRIO, a fully-implemented runtime code manipulation system that supports code transformations on any part of a program, while it executes. DynamoRIO uses code caching technology to provide efficient, transparent, and comprehensive manipulation of an unmodified application running on a stock operating system and commodity hardware. DynamoRIO executes large, complex, modern applications with dynamically-loaded, generated, or even modified code. Despite the formidable obstacles inherent in the IA-32 architecture, DynamoRIO provides these capabilities efficiently, with zero to thirty percent time and memory overhead on both Windows and Linux. 
DynamoRIO exports an interface for building custom runtime code manipulation tools of all types. It has been used by many researchers, with several hundred downloads of our public release, and is being commercialized in a product for protection against remote security exploits, one of numerous applications of runtime code manipulation. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)

/pdf/efficient-transparent-and-comprehensive-runtime-code-junbqv2xwa.pdf

Efficient, transparent, and comprehensive runtime code manipulation

Food recognition is difficult because food items are de-formable objects that exhibit significant variations in appearance. We believe the key to recognizing food is to exploit the spatial relationships between different ingredients (such as meat and bread in a sandwich). We propose a new representation for food items that calculates pairwise statistics between local features computed over a soft pixellevel segmentation of the image into eight ingredient types. We accumulate these statistics in a multi-dimensional histogram, which is then used as a feature vector for a discriminative classifier. Our experiments show that the proposed representation is significantly more accurate at identifying food than existing methods.

/pdf/food-recognition-using-statistics-of-pairwise-local-features-4fvu99eb0n.pdf

Food recognition using statistics of pairwise local features

The capacity and cost-per-bit of DRAM have historically scaled to satisfy the needs of increasingly large and complex computer systems. However, DRAM latency has remained almost constant, making memory latency the performance bottleneck in today's systems. We observe that the high access latency is not intrinsic to DRAM, but a trade-off made to decrease cost-per-bit. To mitigate the high area overhead of DRAM sensing structures, commodity DRAMs connect many DRAM cells to each sense-amplifier through a wire called a bitline. These bitlines have a high parasitic capacitance due to their long length, and this bitline capacitance is the dominant source of DRAM latency. Specialized low-latency DRAMs use shorter bitlines with fewer cells, but have a higher cost-per-bit due to greater sense-amplifier area overhead. In this work, we introduce Tiered-Latency DRAM (TL-DRAM), which achieves both low latency and low cost-per-bit. In TL-DRAM, each long bitline is split into two shorter segments by an isolation transistor, allowing one segment to be accessed with the latency of a short-bitline DRAM without incurring high cost-per-bit. We propose mechanisms that use the low-latency segment as a hardware-managed or software-managed cache. Evaluations show that our proposed mechanisms improve both performance and energy-efficiency for both single-core and multi-programmed workloads.

/pdf/tiered-latency-dram-a-low-latency-and-low-cost-dram-4mlysty4tm.pdf

Tiered-latency DRAM: A low latency and low cost DRAM architecture

Microprocessors are designed to provide good average performance over a variety of workloads. This can lead to inefficiencies both in power and performance for individual programs and during individual phases within the same program. Microarchitectures with multi-configuration units (e.g. caches, predictors, instruction windows) are able to adapt dynamically to program behavior and enable/disable resources as needed. A key element of existing configuration algorithms is adjusting to program phase changes. This is typically done by "tuning" when a phase change is detected --- i.e. sequencing through a series of trial configurations and selecting the best.Algorithms that dynamically collect and analyze program working set information are studied. To make this practical, we propose working set signatures --- highly compressed working set representations (e.g. 32-128 bytes total). Algorithms use working set signatures to 1) detect working set changes and trigger re-tuning; 2) identify, recurring working sets and re-install saved optimal reconfigurations, thus avoiding the time-consuming tuning process; 3) estimate working set sizes to configure caches directly to the proper size, also avoiding the tuning process. Multi-configuration instruction caches are used to demonstrate the performance of the proposed algorithms. When applied to reconfigurable instruction caches, an algorithm that identifies recurring phases achieves power savings and performance similar to the best algorithm reported to date, but with orders-of-magnitude savings in the number of re-tunings.

/pdf/managing-multi-configuration-hardware-via-dynamic-working-wr3bf79i5a.pdf

Managing multi-configuration hardware via dynamic working set analysis

The growing disparity between processor and memory performance has made cache misses increasingly expensive. Additionally, data and instruction caches are not always used efficiently, resulting in large numbers of cache misses. Therefore, the importance of cache performance improvements at each level of the memory hierarchy will continue to grow. In numeric programs, there are several known compiler techniques for optimizing data cache performance. However, integer (nonnumeric) programs often have irregular access patterns that are more difficult for the compiler to optimize. In the past, cache management techniques such as cache bypassing were implemented manually at the machine-language-programming level. As the available chip area grows, it makes sense to spend more resources to allow intelligent control over the cache management. In this paper, we present an approach to improving cache effectiveness, taking advantage of the growing chip area, utilizing run-time adaptive cache management techniques, optimizing both performance and cost of implementation. Specifically, we are aiming to increase data cache effectiveness for integer programs. We propose a microarchitecture scheme where the hardware determines data placement within the cache hierarchy based on dynamic referencing behavior. This scheme is fully compatible with existing instruction set architectures. This paper examines the theoretical upper bounds on the cache hit ratio that cache bypassing can provide for integer applications, including several Windows applications with OS activity. Then, detailed trace-driven simulations of the integer applications are used to show that the implementation described in this paper can achieve performance close to that of the upper bound.

Run-time cache bypassing

Wide-issue processors continue to achieve higher performance by exploiting greater instruction-level parallelism. Dynamic techniques such as out-of-order execution and hardware speculation have proven effective at increasing instruction throughput. Runtime optimization promises to provide an even higher level of performance by adaptively applying aggressive code transformations on a larger scope. This paper presents a new hardware mechanism for generating and deploying runtime optimized code. The mechanism can be viewed as a filtering system that resides in the retirement stage of the processor pipeline, accepts an instruction execution stream as input, and produces instruction profiles and sets of linked, optimized traces as output. The code deployment mechanism uses an extension to the branch prediction mechanism to migrate execution into the new code without modifying the original code. These new components do not add delay to the execution of the program except during short bursts of reoptimization. This technique provides a strong platform for runtime optimization because the hot execution regions are extracted, optimized, and written to main memory for execution and because these regions persist across context switches. The current design of the framework supports a suite of optimizations, including partial function inlining (even into shared libraries), code straightening optimizations, loop unrolling, and peephole optimizations.

An architectural framework for runtime optimization

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Run-time cache bypassing

An architectural framework for runtime optimization