A Survey on Thread-Level Speculation Techniques

Producción CientíficaThread-Level Speculation (TLS) is a promising technique that allows the parallel execution of sequential code without relying on a prior, compile-time-dependence analysis. In this work, we introduce the technique, present a taxonomy of TLS solutions, and summarize and put into perspective the most relevant advances in this field.MICINN (Spain) and ERDF program of the European Union: HomProg-HetSys project (TIN2014-58876-P), CAPAP-H5 network (TIN2014-53522-REDT), and COST Program Action IC1305: Network for Sustainable Ultrascale Computing (NESUS)

Aggressive and reliable high-performance architectures - techniques for thermal control, energy efficiency, and performance augmentation

As more and more transistors fit in a single chip, consumers of the electronics industry continue to expect decline in cost-per-function. Advancements in process technology offer steady improvements in system performance. The improvements manifest themselves as shrinking area, faster circuits and improved battery life. However, this migration toward sub-micro/nano-meter technologies presents a new set of challenges as the system becomes extremely sensitive to any voltage, temperature or process variations. One approach to immunize the system from the adverse effects of these variations is to add sufficient safety margins to the operating clock frequency of the system. Clearly, this approach is overly conservative because these worst case scenarios rarely occur. But, process technology in nanoscale era has already hit the power and frequency walls. Regardless of any of these challenges, the present processors not only need to run faster, but also cooler and use lesser energy. At a juncture where there is no further improvement in clock frequency is possible, data dependent latching through Timing Speculation (TS) provides a silver lining. Timing speculation is a widely known method for realizing better-than-worst-case systems. TS is aggressive in nature, where the mechanism is to dynamically tune the system frequency beyond the worst-case limits obtained from application characteristics to enhance the performance of system-on-chips (SoCs). However, such aggressive tuning has adverse consequences that need to be overcome. Power dissipation, on-chip temperature and reliability are key issues that cannot be ignored. A carefully designed power management technique combined with a reliable, controlled, aggressive clocking not only attempts to constrain power dissipation within a limit, but also improves performance whenever possible. In this dissertation, we present a novel power level switching mechanism by redefining the existing voltage-frequency pairs. We introduce an aggressive yet reliable framework for energy efficient thermal control. We were able to achieve up to 40% speed-up compared to a base scheme without overclocking. We compare our method against different schemes. We observe that up to 75% Energy-Delay squared product (ED2) savings relative to base architecture is possible. We showcase the loss of efficiency in present chip multiprocessor systems due to excess power supplied, and propose Utilization-aware Task Scheduling (UTS) - a power management scheme that increases energy efficiency of chip multiprocessors. Our experiments demonstrate that UTS along with aggressive timing speculation squeezes out maximum performance from the system without loss of efficiency, and breaching power & thermal constraints. From our evaluation we infer that UTS improves performance by up to 12% due to aggressive power level switching and over 50% in ED2 savings compared to traditional power management techniques. Aggressive clocking systems having TS as their central theme operate at a clock frequency range beyond specified safe limits, exploiting the data dependence on circuit critical paths. However, the margin for performance enhancement is restricted due to extreme difference between short paths and critical paths. In this thesis, we show that increasing the lengths of short paths of the circuit increases the margin of TS, leading to performance improvement in aggressively designed systems. We develop Min-arc algorithm to efficiently add delay buffers to selected short paths while keeping down the area penalty. We show that by using our algorithm, it is possible to increase the circuit contamination delay by up to 30% without affecting the propagation delay, with moderate area overhead. We also explore the possibility of increasing short path delays further by relaxing the constraint on propagation delay, and achieve even higher performance. Overall, we bring out the inter-relationship between power, temperature and reliability of aggressively clocked systems. Our main objective is to achieve maximal performance benefits and improved energy efficiency within thermal constraints by effectively combining dynamic frequency scaling, dynamic voltage scaling and reliable overclocking. We provide solutions to improve the existing power management in chip multiprocessors to dynamically maximize system utilization and satisfy the power constraints within safe thermal limits

A scalable architecture for ordered parallelism

We present Swarm, a novel architecture that exploits ordered irregular parallelism, which is abundant but hard to mine with current software and hardware techniques. In this architecture, programs consist of short tasks with programmer-specified timestamps. Swarm executes tasks speculatively and out of order, and efficiently speculates thousands of tasks ahead of the earliest active task to uncover ordered parallelism. Swarm builds on prior TLS and HTM schemes, and contributes several new techniques that allow it to scale to large core counts and speculation windows, including a new execution model, speculation-aware hardware task management, selective aborts, and scalable ordered commits. We evaluate Swarm on graph analytics, simulation, and database benchmarks. At 64 cores, Swarm achieves 51--122× speedups over a single-core system, and out-performs software-only parallel algorithms by 3--18×.National Science Foundation (U.S.) (Award CAREER-145299

Energy-Efficient Acceleration of Asynchronous Programs.

Asynchronous or event-driven programming has become the dominant programming model in the last few years. In this model, computations are posted as events to an event queue from where they get processed asynchronously by the application. A huge fraction of computing systems built today use asynchronous programming. All the Web 2.0 JavaScript applications (e.g., Gmail, Facebook) use asynchronous programming. There are now more than two million mobile applications available between the Apple App Store and Google Play, which are all written using asynchronous programming. Distributed servers (e.g., Twitter, LinkedIn, PayPal) built using actor-based languages (e.g., Scala) and platforms such as node.js rely on asynchronous events for scalable communication. Internet-of-Things (IoT), embedded systems, sensor networks, desktop GUI applications, etc., all rely on the asynchronous programming model. Despite the ubiquity of asynchronous programs, their unique execution characteristics have been largely ignored by conventional processor architectures, which have remained heavily optimized for synchronous programs. Asynchronous programs are characterized by short events executing varied tasks. This results in a large instruction footprint with little cache locality, severely degrading cache performance. Also, event execution has few repeatable patterns causing poor branch prediction. This thesis proposes novel processor optimizations exploiting the unique execution characteristics of asynchronous programs for performance optimization and energy-efficiency. These optimizations are designed to make the underlying hardware aware of discrete events and thereafter, exploit the latent Event-Level Parallelism present in these applications. Through speculative pre-execution of future events, cache addresses and branch outcomes are recorded and later used for improving cache and branch predictor performance. A hardware instruction prefetcher specialized for asynchronous programs is also proposed as a comparative design direction.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120780/1/gauravc_1.pd

An energy-efficient memory unit for clustered microarchitectures

Whereas clustered microarchitectures themselves have been extensively studied, the memory units for these clustered microarchitectures have received relatively little attention. This article discusses some of the inherent challenges of clustered memory units and shows how these can be overcome. Clustered memory pipelines work well with the late allocation of load/store queue entries and physically unordered queues. Yet this approach has characteristic problems such as queue overflows and allocation patterns that lead to deadlocks. We propose techniques to solve each of these problems and show that a distributed memory unit can offer significant energy savings and speedups over a centralized unit. For instance, compared to a centralized cache with a load/store queue of 64/24 entries, our four-cluster distributed memory unit with load/store queues of 16/8 entries each consumes 31 percent less energy and performs 4,7 percent better on SPECint and consumes 36 percent less energy and performs 7 percent better for SPECfp.Peer ReviewedPostprint (author's final draft

Analysis and Approximation of Optimal Co-Scheduling on CMP

In recent years, the increasing design complexity and the problems of power and heat dissipation have caused a shift in processor technology to favor Chip Multiprocessors. In Chip Multiprocessors (CMP) architecture, it is common that multiple cores share some on-chip cache. The sharing may cause cache thrashing and contention among co-running jobs. Job co-scheduling is an approach to tackling the problem by assigning jobs to cores appropriately so that the contention and consequent performance degradations are minimized. This dissertation aims to tackle two of the most prominent challenges in job co-scheduling.;The first challenge is in the computational complexity for determining optimal job co-schedules. This dissertation presents one of the first systematic analyses on the complexity of job co-scheduling. Besides proving the NP completeness of job co-scheduling, it introduces a set of algorithms, based on graph theory and Integer/Linear Programming, for computing optimal co-schedules or their lower bounds in scenarios with or without job migrations. For complex cases, it empirically demonstrates the feasibility for approximating the optimal schedules effectively by proposing several heuristics-based algorithms. These discoveries facilitate the assessment of job co-schedulers by providing necessary baselines, and shed insights to the development of practical co-scheduling systems.;The second challenge resides in the prediction of the performance of processes co-running on a shared cache. This dissertation explores the influence on co-run performance prediction imposed by co-runners, program inputs, and cache configurations. Through a sequence of formal analysis, we derive an analytical co-run locality model, uncovering the inherent statistical connections between the data references of programs single-runs and their co-run locality. The model offers theoretical insights on co-run locality analysis and leads to a lightweight approach for fast prediction of shared cache performance. We demonstrate the effectiveness of the model in enabling proactive job co-scheduling.;Together, the two-dimensional findings open up many new opportunities for cache management on modern CMP by laying the foundation for job co-scheduling, and enhancing the understanding to data locality and cache sharing significantly

Mitosis based speculative multithreaded architectures

In the last decade, industry made a right-hand turn and shifted towards multi-core processor designs, also known as Chip-Multi-Processors (CMPs), in order to provide further performance improvements under a reasonable power budget, design complexity, and validation cost. Over the years, several processor vendors have come out with multi-core chips in their product lines and they have become mainstream, with the number of cores increasing in each processor generation. Multi-core processors improve the performance of applications by exploiting Thread Level Parallelism (TLP) while the Instruction Level Parallelism (ILP) exploited by each individual core is limited. These architectures are very efficient when multiple threads are available for execution. However, single-thread sections of code (single-thread applications and serial sections of parallel applications) pose important constraints on the benefits achieved by parallel execution, as pointed out by Amdahl’s law. Parallel programming, even with the help of recently proposed techniques like transactional memory, has proven to be a very challenging task. On the other hand, automatically partitioning applications into threads may be a straightforward task in regular applications, but becomes much harder for irregular programs, where compilers usually fail to discover sufficient TLP. In this scenario, two main directions have been followed in the research community to take benefit of multi-core platforms: Speculative Multithreading (SpMT) and Non-Speculative Clustered architectures. The former splits a sequential application into speculative threads, while the later partitions the instructions among the cores based on data-dependences but avoid large degree of speculation. Despite the large amount of research on both these approaches, the proposed techniques so far have shown marginal performance improvements. In this thesis we propose novel schemes to speed-up sequential or lightly threaded applications in multi-core processors that effectively address the main unresolved challenges of previous approaches. In particular, we propose a SpMT architecture, called Mitosis, that leverages a powerful software value prediction technique to manage inter-thread dependences, based on pre-computation slices (p-slices). Thanks to the accuracy and low cost of this technique, Mitosis is able to effectively parallelize applications even in the presence of frequent dependences among threads. We also propose a novel architecture, called Anaphase, that combines the best of SpMT schemes and clustered architectures. Anaphase effectively exploits ILP, TLP and Memory Level Parallelism (MLP), thanks to its unique finegrain thread decomposition algorithm that adapts to the available parallelism in the application