Improving single thread performance is a key challenge in modern microprocessors especially because the traditional approach of increasing clock frequency and deep pipelining cannot be pushed further due to power constraints. Therefore, researchers have been looking at unconventional architectures to boost single thread performance without running into the power wall. HW/SW co-designed processors like Nvidia Denver, are emerging as a promising alternative. However, HW/SW co-designed processors need to address some key challenges such as startup delay, providing high performance with simple hardware, translation/optimization overhead, etc. before they can become mainstream. A fundamental requirement for evaluating different design choices and trade-offs to meet these challenges is to have a simulation infrastructure. Unfortunately, there is no such infrastructure available today. Building the aforementioned infrastructure itself poses significant challenges as it encompasses the complexities of not only an architectural framework but also of a compilation one. This paper identifies the key challenges that HW/SW codesigned processors face and the basic requirements for a simulation infrastructure targeting these architectures. Furthermore, the paper presents DARCO, a simulation infrastructure to enable research in this domain.

Dynamically typed programming languages have become very popular in recent years. These languages ease the task of programmers but introduce significant runtime overheads since variables are neither declared nor bound to a particular type. In particular, every time an object property is accessed, we have to obtain the type of the object to which it belongs just before the access, in order to properly compute the address of the property. This adds an important overhead to each of these accesses. In this paper, we present ERICO, a hybrid HW/SW mechanism that removes most of this overhead. This technique is based on a runtime-built structure that provides the information required to identify the addresses of object properties in a very efficient manner. Besides, a hardware cache of this structure stores its most frequently used elements to speedup its access. To demonstrate the effectiveness of this new mechanism, we implement it in a JavaScript engine and show that on average it produces 11% speedup (up to 31% for some applications) and 10.6% energy reduction.

Dynamically typed programming languages have become very popular in the recent years. These languages ease the task of the programmer but introduce significant overheads since assumptions about the types of variables have to be constantly validated at run time. Java Script is a widely used dynamically typed language that has gained significant popularity in recent years. In this paper, we provide a detailed analysis of the two main sources of overhead in the Java Script execution. The first one is the runtime overhead needed for dynamic compilation and housekeeping activities (i.e. Garbage collector, compilation, etc.). The second one is the additional checks and guards introduced by the dynamic nature of Java Script. Then, we propose three new HW/SW optimizations that reduce this latter type of overhead. We show that these two types of overhead represent 35% and 25% respectively of the total execution time on average for a representative workload, and the proposed optimizations provide a 6% average speedup.

Leakage energy is a growing concern in current and future microprocessors. Functional units of microprocessors are responsible for a major fraction of this energy. Therefore, reducing functional unit leakage has received much attention in recent years. Power gating is one of the most widely used techniques to minimize leakage energy. Power gating turns off the functional units during the idle periods to reduce the leakage. Therefore, the amount of leakage energy savings is directly proportional to the idle time duration. This article focuses on increasing the idle interval for the higher SIMD lanes. The applications are profiled dynamically, in a hardware/software codesigned environment, to find the higher SIMD lanes' usage pattern. If the higher lanes need to be turned on for small time periods, the corresponding portion of the code is devectorized to keep the higher lanes off. The devectorized code is executed on the lowest SIMD lane. Our experimental results show that the average energy savings of the proposed mechanism are 15%, 12%, and 71% greater than power gating for SPECFP2006, Physicsbench, and Eigen benchmark suites, respectively. Moreover, the slowdown caused by devectorization is negligible.

An important aspect to consider in applications which work with great volumes of data is that frequently these data are of low quality and also cannot be use other types of data. The field of Soft Computing has dealt, among other things, with developing techniques that will be able to work with these types of low quality data in a suitable way, respecting the true origin of these data. In this paper we present a method to carry out the imputation of missing values from information that may be of low quality when another possibility is not available. The method is based on a predictable model. The imputation method developed is incorporated into the software tool NIP increasing its functionality of imputation/replacement of low quality values.

Compiler based static vectorization is used widely to extract data level parallelism from computation intensive applications. Static vectorization is very effective in vectorizing traditional array based applications. However, compilers inability to reorder ambiguous memory references severely limits vectorization opportunities, especially in pointer rich applications. HW/SW co-designed processors provide an excellent opportunity to optimize the applications at runtime. The availability of dynamic application behavior at runtime will help in capturing vectorization opportunities generally missed by the compilers. This paper proposes to complement the static vectorization with a speculative dynamic vectorizer in a HW/SW co-design processor. We present a speculative dynamic vectorization algorithm that speculatively reorders ambiguous memory references to uncover vectorization opportunities. The hardware checks for any memory dependence violation due to speculative vectorization and takes corrective action in case of violation. Our experiments show that the combined (static + dynamic) vectorization approach provides 2x performance benefit compared to the static vectorization alone, for SPECFP2006. Moreover, dynamic vectorization scheme is as effective in vectorization of pointer-based applications as for the array-based ones, whereas compilers lose significant vectorization opportunities in pointer-based applications.

Leakage power is a growing concern in current and future microprocessors. Functional units of microprocessors are responsible for a major fraction of this power. Therefore, reducing functional unit leakage has received much attention in the recent years. Power gating is one of the most widely used techniques to minimize leakage energy. Power gating turns off the functional units during the idle periods to reduce the leakage. Therefore, the amount of leakage energy savings is directly proportional to the idle time duration. This paper focuses on increasing the idle interval for the higher SIMD lanes. The applications are profiled dynamically, in a HW/SW co-designed environment, to find the higher SIMD lanes usage pattern. If the higher lanes need to be turned-on for small time periods, the corresponding portion of the code is devectorized to keep the higher lanes off. The devectorized code is executed on the lowest SIMD lane. Our experimental results show average SIMD accelerator energy savings of 12% and 24% relative to power gating, for SPECFP2006 and Physics bench. Moreover, the slowdown caused due to devectorization is less than 1%.

Hardware/Software (HW/SW) co-designed processors have emerged as a promising solution to the power and complexity problems of modern microprocessors. These processors utilize dynamic optimizations to improve the performance. However, vectorization, one of the most potent optimizations, has not yet received the deserved attention. This paper presents a speculative dynamic vectorization algorithm to explore its potential.

When individual classifiers are combined appropriately, we usually obtain a better performance in terms of classification precision. Classifier ensembles are the result of combining several individual classifiers. In this work we propose and compare various consensus based combination methods to obtain the final decision of the ensemble based on fuzzy decision trees in order to improve results. We make a comparative study with several datasets to show the efficiency of the various combination methods.

Industry is moving towards multi-core designs as we have hit the memory and power walls. Multi-core designs are very effective to exploit thread-level parallelism (TLP) but do not provide benefits when executing serial code (applications with low TLP, serial parts of a parallel application and legacy code). In this paper we propose Anaphase, a novel approach for speculative multithreading to improve single-thread performance in a multi-core design. The proposed technique is based on a graph partitioning technique which performs a decomposition of applications into speculative threads at instruction granularity. Moreover, the proposed technique leverages communications and pre-computation slices to deal with inter-thread dependences. Results presented in this paper show that this approach improves single-thread performance by 32% on average and up to 2.15x for some selected applications of the Spec2006 suite. In addition, the proposed technique outperforms by 21% on average schemes in which thread decomposition is performed at a coarser granularity.

Industry has shifted towards multi-core designs as we have hit the memory and power walls. However, single thread performance remains of paramount importance since some applications have limited thread-level parallelism (TLP), and even a small part with limited TLP impose important constraints to the global performance, as explained by Amdahl's law.

Both QoS support and congestion management techniques become essential to achieve good network performance in current high-speed interconnection networks. The most effective techniques traditionally considered for both issues, however, require too many resources for being implemented. In this paper we propose a new cost-effective switch architecture able to face the challenges of congestion management and, at the same time, to provide QoS. The efficiency of our proposal is based on using the resources (queues) used by RECN (an efficient Head-Of-Line blocking elimination technique) also for QoS support, without increasing queue requirements. Provided results show that the new switch architecture is able to guarantee QoS levels without any degradation due to congestion situations.

Quality of service (QoS) is becoming an attractive feature for high-performance networks and parallel machines because it could allow a more efficient use of resources. Deadline-based algorithms can provide powerful QoS provision. However, the cost associated with keeping ordered lists of packets makes them impractical for high-performance networks. In this paper, we explore how to adapt efficiently the earliest deadline first family of algorithms to the high-speed networks environments. The results show excellent performance using just two virtual channels, FIFO queues, and a cost feasible with today's technology.

Current interconnect standards providing hardware support for quality of service (QoS) consider up to 16 virtual channels (VCs) for this purpose. However, most implementations do not offer so many VCs because they increase the complexity of the switch and the scheduling delays. We have shown that this number of VCs can be significantly reduced, because it is enough to use two VCs for QoS purposes at each switch port. In this paper, we cover the weaknesses of that proposal and, not only we reduce VCs, but we also improve performance due to the flexibility assigning buffer memory.

Virtual channels (VCs) are a popular solution for the provision of quality of service (QoS). Current interconnect standards propose 16 or even more VCs for this purpose. However, most implementations do not offer so many VCs because it is too expensive in terms of silicon area. Therefore, a reduction of the number of VCs necessary to support QoS can be very helpful in the switch design and implementation. In this paper, we show that this number of VCs can be reduced if the system is considered as a whole rather than each element being taken separately. The scheduling decisions made at network interfaces can be easily reused at switches without significantly altering the global behavior. In this way, we obtain a noticeable reduction of silicon area, component count and, thus, power consumption, and we can provide similar performance to a more complex architecture. We also show that this is a scalable technique, suitable for the foreseen demands of traffic.

Current interconnection standards providing hardware support for quality of service (QoS) consider up to 16 virtual channels (VCs) for this purpose. However, most implementations do not offer so many because VCs increase the complexity of the switch and the scheduling delays. We have shown that this number of VCs can be significantly reduced, because it is enough to use two VCs for QoS purposes at each switch port. In this paper, we explore two alternative switch designs that take advantage of this reduction