Transactional memory represents an attractive conceptual model for programming concurrent applications. Unfortunately, high transaction abort rates can cause significant performance degradation. Conventional transactional memory realizations not only pessimistically abort transactions on every read-write conflict but also because of false sharing, cache evictions, TLB misses, page faults and interrupts. Consequently, the use of transactions needs to be restricted to a very small number of operations to achieve predictable performance, thereby, limiting its benefit to programming simplification. In this paper, we investigate snapshot isolation transactional memory in which transactions operate on memory snapshots that always guarantee consistent reads. By exploiting snapshots, an established database model of transactions, transactions can ignore read-write conflicts and only need to abort on write-write conflicts. Our implementation utilizes a memory controller that supports multiversion memory, to efficiently support snapshotting in hardware.We show that snapshot isolation can reduce the number of aborts in some cases by three orders of magnitude and improve performance by up to 20x.

Currently, GPUs and data parallel processors leverage latency tolerance techniques such as multithreading and prefetching to maximize performance per Watt. However, choosing a technique that provides energy-efficiency on a wide variety of workloads is difficult, as the type of latency to tolerate, required hardware complexity, and energy consumption is directly related to application behavior. After qualitatively evaluating five commonly used latency tolerance techniques, we develop a hybrid technique utilizing multithreading and decoupled execution to maximize performance while minimizing hardware complexity and energy consumption across a wide variety of workloads. We compare our hybrid technique with the five commonly used techniques on a 1024-core data parallel processor by performing a comprehensive design space exploration, leveraging detailed performance and physical design models. By intelligently leveraging both decoupled execution and multithreading, our hybrid latency tolerance technique is able to improve energy-efficiency by 28% to 89% over any single technique on data parallel benchmarks. Compared to other combinations of latency tolerance techniques, we find that our hybrid latency tolerance technique provides the highest energy-efficiency by over 26%.

Programming language and operating system support for efficient concurrency-safe access to shared data is a key concern for the effective use of multi-core processors. Most research has focused on the software model of multiple threads accessing this data within a single shared address space. However, many real applications are actually structured as multiple separate processes for fault isolation and simplified synchronization. In this paper, we describe the HICAMP architecture and its innovative memory system, which supports efficient concurrency safe access to structured shared data without incurring the overhead of inter-process communication. The HICAMP architecture also provides support for programming language and OS structures such as threads, iterators, read-only access and atomic update. In addition to demonstrating that HICAMP is beneficial for multi-process structured applications, our evaluation shows that the same mechanisms provide substantial benefits for other areas, including sparse matrix computations and virtualization.

The design of a digital system for energy efficiency often requires the analysis of circuit tradeoffs in addition to architectural tradeoffs. To assist with this analysis, we present a framework for performing joint exploration of both the architectural and circuit design spaces. In our approach, we use statistical inference techniques to create a model of a large micro-architectural design space from a small number of simulation samples. We then characterize the design tradeoffs of each of the underlying circuits and integrate these with the higher level architectural models to define the joint circuit-architecture design space. We use posynomial forms for all our models, enabling the use of convex optimization tools to efficiently search the joint design space. As an example, we apply this methodology to explore the power-performance tradeoffs in a dual-issue superscalar out-of-order processor, showing how the framework can be used to determine the optimal set of design parameters for energy efficiency. Compared to current architectural tools that use fixed circuit costs, joint optimization can reduce energy by up to 30% by considering circuit tradeoff characteristics.

Power consumption has become a major constraint in the design of processors today. To optimize a processor for energy-efficiency requires an examination of energy-performance trade-offs in all aspects of the processor design space, including both architectural and circuit design choices. In this paper, we apply an integrated architecture-circuit optimization framework to map out energy-performance trade-offs of several different high-level processor architectures. We show how the joint architecture-circuit space provides a trade-off range of approximately 6.5x in performance for 4x energy, and we identify the optimal architectures for different design objectives. We then show that many of the designs in this space come at very high marginal costs. Our results show that, for a large range of design objectives, voltage scaling is effective in efficiently trading off performance and energy, and that the choice of optimal architecture and circuits does not change much during voltage scaling. Finally, we show that with only two designs--a dual-issue in-order design and a dual-issue out-of-order design, both properly optimized-a large part of the energy-performance trade-off space can be covered within 3% of the optimal energy-efficiency.

Due to their high volume, general-purpose processors, and now chip multiprocessors (CMPs), are much more cost effective than ASICs, but lag significantly in terms of performance and energy efficiency. This paper explores the sources of these performance and energy overheads in general-purpose processing systems by quantifying the overheads of a 720p HD H.264 encoder running on a general-purpose CMP system. It then explores methods to eliminate these overheads by transforming the CPU into a specialized system for H.264 encoding. We evaluate the gains from customizations useful to broad classes of algorithms, such as SIMD units, as well as those specific to particular computation, such as customized storage and functional units.

We propose a method of analytically characterizing processor performance as a function of circuit latencies. In our approach, we modify traditional simulation to use variables instead of fixed latencies for the internal functional units. The simulation engine then algebraically computes execution times, and the result is a mathematical equation which characterizes the performance space across numerous processor configurations. We discuss the computational complexity issues of this approach and show that instruction chunking and simple equation redundancy checking can make this approach feasible-we can model a large multi-dimensional design space with thousands to millions of design parameter combinations for about 10times the simulation time of a single conventional simulation run. We demonstrate our approach by exploring two different machines: a traditional MlPS-style in-order pipeline and the Intel Graphics Media Accelerator X3000.

In this paper we explore the relationship between adder topology and energy efficiency. We compare the energy-delay tradeoff curves of selected 32- bit adder topologies, to determine how architectural features and design techniques affect energy efficiency. Optimizing different adders for the supply and threshold voltages, and transistor sizing, we show that topologies with the least number of logic stages having an average fanin of two per stage, and fewest wires are most energy efficient. While a design with fully custom sizes can be extremely tedious to layout, we show that custom sizing can be used as a guide to group different gates in the design, resulting in a manageable layout overhead without significant loss of energy efficiency.