Abstract This work presents the design, analysis and development of VolRec, a volumetric 4D reconstruction system for OCT (optical coherence tomography) data. VolRec is able to obtain and process more than 25 high-resolution OCT data volumes per second. This way, VolRec allows OCT systems to perform real-time measurements that could help doctors and physicians in many different cases, e.g., during surgical interventions or during exams to restless patients, such as kids. Moreover, VolRec allows to adapt to telemedicine techniques such as telesurgery or image-guided medical robots. To achieve real-time performance, it is necessary to address the limitations of current OCT systems, which are restricted by both the acquisition and processing of massive amounts of data. To overcome those limitations, this work proposes an approach based on a 4D reconstruction. Our proposal allows to generate high-resolution volumes from acquired low-resolution ones. Hence, increasing the acquisition speed while reducing the acquisition resolution without sacrificing image quality. To this end, we use parallel programming mechanisms such as OpenMP (for CPUs) and CUDA to exploit the computing capabilities of modern GPUs. Our real-time volumetric reconstruction algorithm efficiently achieves a very high performance, reaching a processing rate of 18 GigaVoxels/s and about 72 $$\times$$ × speedup over a parallel CPU-based algorithm using a GPU, by efficiently exploiting the vast amount of data-level parallelism inherent to OCT data volumes.

The increasing demand for high-quality graphics requires a significant increase in computational power of modern GPUs. The common approach to follow is augmenting the number of compute units (i.e., shader cores). However, this can result in underutilized resources if the workload is not properly balanced. This is particularly challenging in Tile-Based Rendering (TBR) GPUs, the predominant architecture in mobile GPUs, running graphics applications due to limited per-tile workload. This work proposes parallel tile rendering to efficiently in-crease the computational capabilities of TBR GPUs. This solves the problem of not having enough work to utilize the additional compute units but causes memory-intensive applications to underperform due to the increased memory pressure. To this end, we introduce LIBRA, a parallel tile rendering architecture that includes a novel locality-aware approach to schedule tiles to Raster Units to evenly distribute memory requests during the rendering of each frame. This alleviates memory congestion, therefore, reducing memory access time. LIBRA leverages frame-to-frame coherence to predict the memory pressure of each tile of a frame without penalizing the hit ratio of the cache memories. Evaluations over a wide range of commercial gaming applications show that LIBRA reduces the average memory latency by 13.5% and achieves an average speedup of 20.9%. It also provides an 11.4% improvement in throughput (frames per second) and a total GPU energy reduction of 9.2%, while adding negligible overhead.

Literature is plentiful in works exploiting cache locality for GPUs. A majority of them explore replacement or bypassing policies. In this paper, however, we surpass this exploration by fabricating a formal proof for a no-overhead quasi-optimal caching technique for caching textures in graphics workloads. Textures make up a significant part of main memory traffic in mobile GPUs, which contributes to the total GPU energy consumption. Since texture accesses use a shared L2 cache, improving the L2 texture caching efficiency would decrease main memory traffic, thus improving energy efficiency, which is crucial for mobile GPUs. Our proposal reaches quasi-optimality by exploiting the frame-to-frame reuse of textures in graphics. We do this by traversing frames in a boustrophedonic11Boustrophedon is a style of writing in which alternate lines of writing are reversed in order. This is in contrast to most modern languages, where the order of lines is the same, usually left-to-right. manner w.r.t. the frame-to-frame tile order. We first approximate the texture access trace to a circular trace and then forge a formal proof for our proposal being optimal for such traces. We also complement the proof with empirical data that demonstrates the quasi-optimality of our no-cost proposal.

Irregular, memory-intensive applications often incur high translation lookaside buffer (TLB) miss rates that result in significant address translation overheads. Employing huge pages is an effective way to reduce these overheads, however in real systems the number of available huge pages can be limited when system memory is nearly full and/or fragmented. Thus, huge pages must be used selectively to back application memory. This work demonstrates that choosing memory regions that incur the most TLB misses for huge page promotion best reduces address translation overheads. We call these regions High reUse TLB-sensitive data (HUBs). Unlike prior work which relies on expensive per-page software counters to identify promotion regions, we propose new architectural support to identify these regions dynamically at application runtime.

Despite their ubiquity in many important big-data applications, graph analytic kernels continue to challenge modern memory hierarchies due to their frequent, long-latency, pointer indirect accesses to vertex property data. Such accesses exhibit poor locality and variable reuse that trouble cache replacement policies, and consequently increase memory bandwidth pressure. Specialized graph-tailored prefetching mechanisms, processor designs, and memory hierarchy engines have been developed to tolerate the long latencies of such accesses. However, these approaches are either too bandwidth-intensive, require invasive hardware changes that inhibit general-purpose computation flexibility, or rely on software preprocessing that limits true speedup. This work introduces Graphfire, a flexible memory hierarchy approach that learns different access patterns in graph processing and exploits the synergy of specialized fetch, insertion, and replacement optimizations for problematic indirect accesses without relying on software or ISA support. More specifically, Graphfire identifies when these irregular accesses occur and employs tailored access granularities, data-aware insertion, and frequency-based replacement accordingly. It achieves up to a 1.79× speedup (geomean 1.3×) and these improvements scale due to bandwidth efficiency; with 64 cores, Graphfire yields up to a 71.33× speedup (geomean 63.32×) over a single baseline core and allows memory-bound graph analytic codes to scale far beyond prior work.

Cache Replacement Policies are known to have an important impact on hit rates. The OPT replacement policy [27] has been formally proven as optimal for minimizing misses. Due to its need to look far ahead for future memory accesses, it is often reduced to a yardstick for measuring the efficacy of other practical caches. In this paper, we bring the OPT to life, in architectures for mobile GPUs, for which energy efficiency is of great consequence. We also mold other factors in the memory hierarchy to enhance its impact. The end results are a 13.8% decrease in the memory hierarchy energy consumption and an increased throughput in the Tiling Engine. We also observe a 5.5% decrease in the total GPU energy and a 3.7% increase in frames per second (FPS).

Modern computing systems employ significant heterogeneity and specialization to meet performance targets at manageable power. However, memory latency bottlenecks remain problematic, particularly for sparse neural network and graph analytic applications where indirect memory accesses (IMAs) challenge the memory hierarchy.

An important drawback of cycle-accurate microarchitectural simulators is that they are several orders of magnitude slower than the system they model. This becomes an important issue when simulations have to be repeated multiple times sweeping over the desired design space. In the specific context of graphics workloads, performing cycle-accurate simulations are even more demanding due to the high number of triangles that have to be shaded, lighted and textured to compose a single frame. As a result, simulating a few minutes of a video game sequence is extremely time-consuming.In this paper, we make the observation that collecting information about the vertices and primitives that are processed, along with the times that shader programs are invoked, allows us to characterize the activity performed on a given frame. Based on that, we propose a novel methodology for the efficient simulation of graphics workloads called MEGsim, an approach that is capable of accurately characterizing entire video sequences by using a small subset of selected frames which substantially drops the simulation time. For a set of popular Android games, we show that MEGsim achieves an average simulation speedup of 126×, achieving remarkably accurate results for the estimated final statistics, e.g., with average relative errors of just 0.84% for the total number of cycles, 0.99% for the number of DRAM accesses, 1.2% for the number of L2 cache accesses, and 0.86% for the number of L1 (tile cache) accesses.

Contemporary GPU architectures have multiple shader cores and a scheduler that distributes work (threads) among them, focusing on load balancing. These load balancing techniques favor thread distributions that are detrimental to texture memory locality for graphics applications in the L1 Texture Caches. Texture memory accesses make up the majority of the traffic to the memory hierarchy in typical low power graphics architectures. This paper focuses on improving the L1 Texture cache locality by focusing on a new workload scheduler by exploring various methods to group the threads, assign the groups to shader cores and also to reorder threads without violating the correctness of the pipeline. To overcome the resulting load imbalance, we also propose a minor modification in the GPU architecture that helps translate the improvement in cache locality to an improvement in the GPU’s performance. We propose DTexL that envelops these ideas and evaluate it over a benchmark suite of ten commercial games, to obtain a 46.8% decrease in L2 Accesses, a 19.3% increase in performance and a 6.3% decrease in total GPU energy. All this with a negligible overhead.

Modern mobile GPUs integrate an increasing number of shader cores to speedup the execution of graphics workloads. Each core integrates a private Texture Cache to apply texturing effects on objects, which is backed-up by a shared L2 cache. However, as in any other memory hierarchy, such organization produces data replication in the upper levels (i.e., the private Texture Caches) to allow for faster accesses at the expense of reducing their overall effective capacity. E.g., in a mobile GPU with four shader cores, about 84.6% of the requested texture blocks are replicated in at least one of the other private Texture Caches.This paper proposes a novel dynamically-mapped Non-Uniform Cache Architecture (NUCA) organization for the private Texture Caches of a mobile GPU aimed at increasing their effective overall capacity and decreasing the overall access latency by attacking data replication. A block missing in a local Texture Cache may be serviced by a remote one at a cost smaller than a round trip to the shared L2. The proposed Dynamic Texture Mapping-NUCA (DTM-NUCA) features a lightweight mapping table, called Affinity Table, that is independent of the L2 cache size, unlike a traditional NUCA organization. The best owner for a given set of blocks is dynamically determined and stored in the Affinity Table to maximize local accesses. The mechanism also allows for a certain amount of replication to favor local accesses where appropriate, without hurting performance due to the small capacity loss resulting from the allowed replication. DTM-NUCA is presented in two flavors. One with a centralized Affinity Table, and another with a distributed Affinity Table. Experimental results show first that the L2 pressure is effectively reduced, eliminating 41.8% of the L2 accesses on average. As for the average latency, DTM-NUCA performs a very effective job at maximizing local over remote accesses, achieving 73.8% of local accesses on average. As a consequence, our novel DTM-NUCA organization obtains an average speedup of 16.9% and overall 7.6% energy savings over a conventional organization.

This article proposes a novel micro-architecture approach for mobile GPUs aimed at early removing the occluded geometry in a scene by leveraging frame-to-frame coherence, thus reducing the overall energy consumption. Mobile GPUs commonly implement a Tile-Based Rendering (TBR) architecture that differentiates two main phases: the Geometry Pipeline , where all the geometry of a scene is processed; and the Raster Pipeline , where primitives are rendered in a framebuffer. After the Geometry Pipeline, only non-culled primitives inside the camera’s frustum are stored into the Parameter Buffer , a data structure stored in DRAM. However, among the non-culled primitives there is a significant amount that are rendered but non-visible at all , resulting in useless computations. On average, 60% of those primitives are completely occluded in our benchmarks. Despite TBR architectures use on-chip caches for the Parameter Buffer, about 46% of the DRAM traffic still comes from accesses to such buffer. The proposed Triangle Dropping technique leverages the visibility information computed along the Raster Pipeline to predict the primitives’ visibility in the next frame to early discard those that will be totally occluded, drastically reducing Parameter Buffer accesses. On average, our approach achieves overall 14.5% energy savings, 28.2% energy-delay product savings, and a speedup of 20.2%.

In real-time rendering, a 3D scene is modelled with meshes of triangles that the GPU projects to the screen. They are discretized by sampling each triangle at regular space intervals to generate fragments which are then added texture and lighting effects by a shader program. Realistic scenes require detailed geometric models, complex shaders, high-resolution displays and high screen refreshing rates, which all come at a great compute time and energy cost. This cost is often dominated by the fragment shader, which runs for each sampled fragment. Conventional GPUs sample the triangles once per pixel; however, there are many screen regions containing low variation that produce identical fragments and could be sampled at lower than pixel-rate with no loss in quality. Additionally, as temporal frame coherence makes consecutive frames very similar, such variations are usually maintained from frame to frame. This work proposes Dynamic Sampling Rate (DSR), a novel hardware mechanism to reduce redundancy and improve the energy efficiency in graphics applications. DSR analyzes the spatial frequencies of the scene once it has been rendered. Then, it leverages the temporal coherence in consecutive frames to decide, for each region of the screen, the lowest sampling rate to employ in the next frame that maintains image quality. We evaluate the performance of a state-of-the-art mobile GPU architecture extended with DSR for a wide variety of applications. Experimental results show that DSR is able to remove most of the redundancy inherent in the color computations at fragment granularity, which brings average speedups of 1.68x and energy savings of 40%.

Abstract This paper presents an energy-efficient design and evaluation of a novel portable device for the automatic correction of presbyopia in human eyes driven by the use of opto-electronic lenses and based on the dynamic pupil response of the subject. Due to the wearable nature of the proposed Dynamic Auto–Accommodation Glasses, in addition to the real-time requirement, an energy-efficient implementation is critical for the success of the device. In this work, the binocular pupil tracking of a subject, followed by the calculation of the eyes’ vergence, and the control of a pair of opto-electronic lenses are implemented and evaluated on several hardware platforms, including two mobile GPU/SoCs, a high-end FPGA, a low-cost FPGA, and a desktop GPU (as a reference point). The pupil tracking algorithm has been parallelized, applying different platform-specific optimizations for each case, to design a fast yet energy-efficient wearable. The hardware platforms have been evaluated to determine which one is the most appropriate for the presbyopia correction task. The experimental results show that the most energy-efficient platform is a mobile GPU (Samsung Exynos 8890) capable of processing frames at 0.016 Joules/frame, still allowing real-time processing (24 frames/sec).

The most common task of GPUs is to render images in real time. When rendering a 3D scene, a key step is to determine which parts of every object are visible in the final image. There are different approaches to solve the visibility problem, the Z-Test being the most common. A main factor that significantly penalizes the energy efficiency of a GPU, especially in the mobile arena, is the so-called overdraw, which happens when a portion of an object is shaded and rendered but finally occluded by another object. This useless work results in a waste of energy; however, a conventional Z-Test only avoids a fraction of it. In this article we present a novel microarchitectural technique, the Omega-Test, to drastically reduce the overdraw on a Tile-Based Rendering (TBR) architecture. Graphics applications have a great degree of inter-frame coherence, which makes the output of a frame very similar to the previous one. The proposed approach leverages the frame-to-frame coherence by using the resulting information of the Z-Test for a tile (a buffer containing all the calculated pixel depths for a tile), which is discarded by nowadays GPUs, to predict the visibility of the same tile in the next frame. As a result, the Omega-Test early identifies occluded parts of the scene and avoids the rendering of non-visible surfaces eliminating costly computations and off-chip memory accesses. Our experimental evaluation shows average EDP savings in the overall GPU/Memory system of 26.4 percent and an average speedup of 16.3 percent for the evaluated benchmarks.

Graph structures are a natural representation of important and pervasive data. While graph applications have significant parallelism, their characteristic pointer indirect loads to neighbor data hinder scalability to large datasets on multicore systems. A scalable and efficient system must tolerate latency while leveraging data parallelism across millions of vertices. Modern Out-of-Order (OoO) cores inherently tolerate a fraction of long latencies, but become clogged when running severely memory-bound applications. Combined with large power/area footprints, this limits their parallel scaling potential and, consequently, the gains that existing software frameworks can achieve. Conversely, accelerator and memory hierarchy designs provide performant hardware specializations, but cannot support diverse application demands. To address these shortcomings, we present GraphAttack, a hardware-software data supply approach that accelerates graph applications on in-order multicore architectures. GraphAttack proposes compiler passes to (1) identify idiomatic long-latency loads and (2) slice programs along these loads into data Producer/ Consumer threads to map onto pairs of parallel cores. Each pair shares a communication queue; the Producer asynchronously issues long-latency loads, whose results are buffered in the queue and used by the Consumer. This scheme drastically increases memory-level parallelism (MLP) to mitigate latency bottlenecks. In equal-area comparisons, GraphAttack outperforms OoO cores, do-all parallelism, prefetching, and prior decoupling approaches, achieving a 2.87× speedup and 8.61× gain in energy efficiency across a range of graph applications. These improvements scale; GraphAttack achieves a 3× speedup over 64 parallel cores. Lastly, it has pragmatic design principles; it enhances in-order architectures that are gaining increasing open-source support.

Soft Error Rate (SER) estimation is an important challenge for integrated circuits because of the increased vulnerability brought by technology scaling. This paper presents a methodology to estimate in early stages of the design the susceptibility of combinational circuits to particle strikes. In the core of the framework liesMASkIt, a novel approach that combines signal probabilities with technology characterization to swiftly compute the logical, electrical, and timing masking effects of the circuit under study taking into account all input combinations and pulse widths at once. Signal probabilities are estimated applying a new hybrid approach that integrates heuristics along with selective simulation of reconvergent subnetworks. The experimental results validate our proposed technique, showing a speedup of two orders of magnitude in comparison with traditional fault injection estimation with an average estimation error of 5 percent. Finally, we analyze thevulnerabilityof the Decoder, Scheduler, ALU, and FPU of an out-of-order, superscalar processor design.

As Moore's Law has slowed and Dennard Scaling has ended, architects are increasingly turning to heterogeneous parallelism and hardware-software co-design. These trends present new challenges for simulation-based performance assessments that are central to early-stage architectural exploration. Simulators must be lightweight to support heterogeneous combinations of general-purpose cores and specialized processing units. They must also support agile exploration of hardware-software co-design, i.e. changes in the programming model, compiler, ISA, and specialized hardware.

As Moore's Law has slowed and Dennard Scaling has ended, architects are increasingly turning to heterogeneous parallelism and domain-specific hardware-software co-designs. These trends present new challenges for simulation-based performance assessments that are central to early-stage architectural exploration. Simulators must be lightweight to support rich heterogeneous combinations of general purpose cores and specialized processing units. They must also support agile exploration of hardware-software co-design, i.e. changes in the programming model, compiler, ISA, and specialized hardware. To meet these challenges, we introduce MosaicSim, a lightweight, modular simulator for heterogeneous systems, offering accuracy and agility designed specifically for hardware-software co-design explorations. By integrating the LLVM toolchain, MosaicSim enables efficient modeling of instruction dependencies and flexible additions across the stack. Its modularity also allows the composition and integration of different hardware components. We first demonstrate that MosaicSim captures architectural bottlenecks in applications, and accurately models both scaling trends in a multicore setting and accelerator behavior. We then present two case-studies where MosaicSim enables straightforward design space explorations for emerging systems, i.e. data science application acceleration and heterogeneous parallel architectures.

GPUs' main workload is real-time image rendering. These applications take a description of a (animated) scene and produce the corresponding image(s). An image is rendered by computing the colors of all its pixels. It is normal that multiple objects overlap at each pixel. Consequently, a significant amount of processing is devoted to objects that will not be visible in the final image, in spite of the widespread use of the Early Depth Test in modern GPUs, which attempts to discard computations related to occluded objects. Since animations are created by a sequence of similar images, visibility usually does not change much across consecutive frames. Based on this observation, we present Early Visibility Resolution (EVR), a mechanism that leverages the visibility information obtained in a frame to predict the visibility in the following one. Our proposal speculatively determines visibility much earlier in the pipeline than the Early Depth Test. We leverage this early visibility estimation to remove ineffectual computations at two different granularities: pixel-level and tile-level. Results show that such optimizations lead to 39% performance improvement and 43% energy savings for a set of commercial Android graphics applications running on stateof-the-art mobile GPUs.

GPUs are one of the most energy-consuming components for real-time rendering applications, since a large number of fragment shading computations and memory accesses are involved. Main memory bandwidth is especially taxing battery-operated devices such as smart-phones. TileBased Rendering GPUs divide the screen space into multiple tiles that are independently rendered in on-chip buffers, thus reducing memory bandwidth and energy consumption. We have observed that, in many animated graphics workloads, a large number of screen tiles have the same color across adjacent frames. In this paper, we propose Rendering Elimination (RE), a novel micro-architectural technique that accurately determines if a tile will be identical to the same tile in the preceding frame before rasterization by means of comparing signatures. Since RE identifies redundant tiles early in the graphics pipeline, it completely avoids the computation and memory accesses of the most power consuming stages of the pipeline, which substantially reduces the execution time and the energy consumption of the GPU. For widely used Android applications, we show that RE achieves an average speedup of 1.74x and energy reduction of 43% for the GPU/Memory system, surpassing by far the benefits of Transaction Elimination, a state-of-the-art memory bandwidth reduction technique available in some commercial Tile-Based Rendering GPUs.

Hartmann–Shack aberrometry is a widely used technique in the field of visual optics but, high-speed and accurate processing of Hartmann–Shack images can be a computationally expensive/resource intensive task. While some advancements have been made in achieving high-performance processing units, they have not been specifically designed for processing Hartmann–Shack images of the human eye with Graphics Processing Units. In this work, we present the first full-Graphics Processing Unit implementation of a Hartmann–Shacksensor algorithm aimed at accurately measuring ocular aberrations at a high speed from high-resolution spot pattern images. The proposed algorithm, called PaPyCS (Parallel Pyramidal Centroid Search), is inherently parallel and performs a very robust centroid search to avoid image noise and other artifacts. This is a field where the use of Graphics Processing Units have not been exploited despite the fact that they can boost Adaptive Optics systems and related closed-loop approaches. Our proposed implementation achieves processing speeds of 380 frames per second for high resolution (1280x1280 pixels) images, in addition to showing a high resilience to system and image artifacts that appear in Hartmann–Shack images from human eyes: more than 98% of the Hartmann–Shack images, with aberrations of up to 4 μ m Root Mean Square for a 5.12mm pupil diameter, were measured with less than 0.05 μ m Root Mean Square Error, which is basically negligible for ocular aberrations.

Decoupling techniques have been proposed to reduce the amount of memory latency exposed to high-performance accelerators as they fetch data. Although decoupled access-execute (DAE) and more recent decoupled data supply approaches offer promising single-threaded performance improvements, little work has considered how to extend them into parallel scenarios. This article explores the opportunities and challenges of designing parallel, high-performance, resource-efficient decoupled data supply systems. We propose M ercury , a parallel decoupled data supply system that utilizes thread-level parallelism for high-throughput data supply with good portability attributes. Additionally, we introduce some microarchitectural improvements for data supply units to efficiently handle long-latency indirect loads.

An efficient and robust pupil tracking system is an important tool in visual optics and ophthalmology. It is also central to techniques for gaze tracking, of use in psychological and medical research, marketing, human–computer interaction, virtual reality and other areas. A typical setup for pupil tracking includes a camera linked to infrared LED illumination. In this work, we evaluate and parallelize several pupil tracking algorithms with the aim of accurately estimating the pupil position and size in both eyes simultaneously, to be applied in a high-speed binocular pupil tracking system. To achieve high processing speed, the original non-parallel algorithms have been parallelized by using CUDA and OpenMP. Modern graphics processors are designed to process images at high temporal frequencies and spatial resolution, and CUDA enables them to be used for general-purpose computing. Our implementation allows for efficient binocular pupil tracking at high speeds for high-resolution images (up to 988 fps with images of $$1280\times 1024$$ pixels) using a state-of-the-art GPU.

In today’s computers, heterogeneous processing is used to meet performance targets at manageable power. In adopting increased compute specialization, however, the relative amount of time spent on communication increases. System and software optimizations for communication often come at the costs of increased complexity and reduced portability. The Decoupled Supply-Compute (DeSC) approach offers a way to attack communication latency bottlenecks automatically, while maintaining good portability and low complexity. Our work expands prior Decoupled Access Execute techniques with hardware/software specialization. For a range of workloads, DeSC offers roughly 2 × speedup, and additional specialized compression optimizations reduce traffic between decoupled units by 40%.

Integrated circuits are getting increasingly vulnerable to soft errors; as a consequence, soft error rate (SER) estimation has become an important and very challenging goal. In this work, a novel approach for SER estimation of combinational circuits is presented. The proposed framework is divided in two stages. First, signal probabilities are computed via a hybrid approach combining heuristics and selective simulation of reconvergent subnets. In the second stage, signal probabilities are used to compute the vulnerability of all the gates in a combinational block using a backward-traversing algorithm that takes into account logical, electrical and timing masking factors. Experimental results show that our signal probability estimation approach, in comparison with similar techniques in the literature, reduces inaccuracy by 96% while adding minimal execution time overhead. In addition, results indicate that our framework is two orders of magnitude faster than traditional Monte Carlo-based fault injection with minor loss in accuracy in both signal probability and SER estimation (average error of 5%).

Today's computers employ significant heterogeneity to meet performance targets at manageable power. In adopting increased compute specialization, however, the relative amount of time spent on memory or communication latency has increased. System and software optimizations for memory and communication often come at the costs of increased complexity and reduced portability. We propose Decoupled Supply-Compute (DeSC) as a way to attack memory bottlenecks automatically, while maintaining good portability and low complexity. Drawing from Decoupled Access Execute (DAE) approaches, our work updates and expands on these techniques with increased specialization and automatic compiler support. Across the evaluated workloads, DeSC offers an average of 2.04x speedup over baseline (on homogeneous CMPs) and 1.56x speedup when a DeSC data supplier feeds data to a hardware accelerator. Achieving performance very close to what a perfect cache hierarchy would offer, DeSC offers the performance gains of specialized communication acceleration while maintaining useful generality across platforms.

Pupil tracking under infrared illumination is an important tool for many researchers in physiological visual optics and ophthalmology. It is also a relevant topic for gaze tracking which is used in psychological and medical research, marketing, human-computer interaction, virtual reality and other areas. A typical setup can be either a low-cost webcam with some infrared LEDs or glasses with mounted cameras and infrared illumination. In this work, we evaluate and parallelize several pupil tracking algorithms with the aim of estimating the pupil's position and size with high accuracy in order to develop a high-speed pupil tracking system. To achieve high processing speed the original non-parallel algorithms have been parallelized by using CUDA and OpenMP. Graphics cards are designed to process images at very high frequencies and resolutions, and CUDA enables them to be used for general purpose computing. Our experimental results show that pupil tracking can be efficiently performed at high speeds with high-resolution images (up to 530 Hz with images of 1280x1024 pixels) using a state-of-the-art GP-GPU.

The traditional performance cost benefits we have enjoyed for decades from technology scaling are challenged by several critical constraints including reliability. Increases in static and dynamic variations are leading to higher probability of parametric and wear-out failures and are elevating reliability into a prime design constraint. In particular, SRAM cells used to build caches that dominate the processor area are usually minimum sized and more prone to failure. It is therefore of paramount importance to develop effective methodologies that facilitate the exploration of reliability techniques for caches. To this end, we present an analytical model that can determine for a given cache configuration, address trace, and random probability of permanent cell failure the exact expected miss rate and its standard deviation when blocks with faulty bits are disabled. What distinguishes our model is that it is fully analytical, it avoids the use of fault maps, and yet, it is both exact and simpler than previous approaches. The analytical model is used to produce the miss-rate trends ( expected miss-rate ) for future technology nodes for both uncorrelated and clustered faults. Some of the key findings based on the proposed model are (i) block disabling has a negligible impact on the expected miss-rate unless probability of failure is equal or greater than 2.6e-4, (ii) the fault map methodology can accurately calculate the expected miss-rate as long as 1,000 to 10,000 fault maps are used, and (iii) the expected miss-rate for execution of parallel applications increases with the number of threads and is more pronounced for a given probability of failure as compared to sequential execution.

Technology scaling improvement is affecting the reliability of ICs due to increases in static and dynamic variations as well as wear-out failures. This is particularly true for caches that dominate the area of modern processors and are built with minimum-sized, but prone to failure, SRAM cells. Our attempt to address this cache reliability challenge is an analytical model for determining the implications on cache miss-rate of block-disabling due to random cell failure. The proposed model is distinct from previous work in that is an exact model rather than an approximation and yet it is simpler than previous work. Its simplicity stems from the lack of fault-maps in the analysis. The model capabilities are illustrated through a study of cache miss-rate trends in future technology nodes. The model is also used to determine the accuracy of a random fault map methodology. The analysis reveals, for the assumptions, programs and cache configuration used in this study, a surprising result: a relative small number of random fault maps, 100–1000, is sufficient to obtain accurate mean and standard-deviation values for the miss-rate. Additional investigation revealed that the cause of this behavior is a high correlation between the number of accesses and access distribution between cache sets.

In the recent years virtually all processor architectures employ multiple cores per chip (CMPs). It is possible to use legacy (i.e., single-core) power saving techniques in CMPs which run either sequential applications or independent multithreaded workloads. However, new challenges arise when running parallel shared-memory applications. In the later case, sacrificing some performance in a single core (thread) in order to be more energy-efficient might unintentionally delay the rest of cores (threads) due to synchronization points (locks/barriers), therefore, harming the performance of the whole application. CMPs increasingly face thermal and power-related problems during their typical use. Such problems can be solved by setting a power budget to the processor/core. This paper initially studies the behavior of different techniques to match a predefined power budget in a CMP processor. While legacy techniques properly work for thread independent/multi-programmed workloads, parallel workloads exhibit the problem of independently adapting the power of each core in a thread dependent scenario. In order to solve this problem we propose a novel mechanism, Power Token Balancing (PTB), aimed at accurately matching an external power constraint by balancing the power consumed among the different cores using a power token-based approach while optimizing the energy efficiency. We can use power (seen as tokens or coupons) from non-critical threads for the benefit of critical threads. PTB runs transparent for thread independent / multiprogrammed workloads and can be also used as a spin lock detector based on power patterns. Results show that PTB matches more accurately a predefined power budget (total energy consumed over the budget is reduced to 8\% for a 16-core CMP) than DVFS with only a 3\% energy increase. Finally, we can trade accuracy on matching the power budget for energy-efficiency reducing the energy a 4% with a 20% of accuracy.

CMOS scaling exacerbates hardware errors making reliability a big concern for recent and future microarchitecture designs. Mechanisms to provide fault tolerance in architectures must accomplish several objectives such as low performance degradation, power consumption and area overhead. Several studies have been already proposed to provide fault tolerance for parallel codes. However, these proposals are usually implemented over non-realistic environments including the use of shared-buses among processors or modifying highly optimized hardware designs such as caches. Our main design goal is to provide transient fault detection and recovery while modifying hardware as less as possible. To this end, we propose LBRA based on a Hardware Transactional Memory (HTM) architecture in which two redundant threads successfully detects and recovers from transient faults, assuring a consistent view of the memory by means of a pair-shared cacheable virtual memory log which keeps the computation results. Results show that our log-based mechanism introduces a small performance degradation of 5% in a non-faulty scenario. Additionally, we show that LBRA supports huge fault rates such as 100 faults per million of cycles with low additional performance degradation.

In the last years high performance processor designs have evolved toward Chip-Multiprocessor (CMP) architectures that implement multiple processing cores on a single die. As the number of cores inside a CMP increases, the on-chip interconnection network will have significant impact on both overall performance and power consumption as previous studies have shown. On the other hand, CMP designs are likely to be equipped with latency hiding techniques like hardware prefetching in order to reduce the negative impact on performance that, otherwise, high cache miss rates would lead to. Unfortunately, the extra number of network messages that prefetching entails can drastically increase the amount of power consumed in the interconnect. In this work, we show how to reduce the impact of prefetching techniques in terms of power (and energy) consumption in the context of tiled CMPs. Our proposal is based on the fact that the wires used in the on-chip interconnection network can be designed with varying latency, bandwidth and power characteristics. By using a heterogeneous interconnect, where low-power wires are used for dealing with prefetched lines, significant energy savings can be obtained. Detailed simulations of a 16-core CMP show that our proposal obtains improvements of up to 30% in the power consumed by the interconnect (15-23% on average) with almost negligible cost in terms of execution time (average degradation of 2%).

Continuous improvements in integration scale have made major microprocessor vendors to move to designs that integrate several processing cores on the same chip. Chip multiprocessors (CMPs) constitute a good alternative to traditional monolithic designs for several reasons, among others, better levels of performance, scalability, and performance/energy ratio. On the other hand, higher clock frequencies and the increasing transistor density have revealed power dissipation and temperature as critical design issues in current and future architectures. Previous studies have shown that the interconnection network of a Chip Multiprocessor (CMP) has significant impact on both overall performance and energy consumption. Moreover, wires used in such interconnect can be designed with varying latency, bandwidth, and power characteristics. In this work, we show how messages can be efficiently managed, from the point of view of both performance and energy, in tiled CMPs using a heterogeneous interconnect. Our proposal consists of two approaches. The first is Reply Partitioning, a technique that splits replies with data into a short Partial Reply message that carries a subblock of the cache line that includes the word requested by the processor plus an Ordinary Reply with the full cache line. This technique allows all messages used to ensure coherence between the L1 caches of a CMP to be classified into two groups: critical and short, and noncritical and long. The second approach is the use of a heterogeneous interconnection network composed of low-latency wires for critical messages and low-energy wires for noncritical ones. Detailed simulations of 8 and 16-core CMPs show that our proposal obtains average savings of 7 percent in execution time and 70 percent in the Energy-Delay squared Product (ED2P) metric of the interconnect over previous works (from 24 to 30 percent average ED2P improvement for the full CMP). Additionally, the sensitivity analysis shows that although the execution time is minimized for subblocks of 16 bytes, the best choice from the point of view of the ED2P metric is the 4-byte subblock configuration with an additional improvement of 2 percent over the 16-byte one for the ED2P metric of the full CMP.

In the past years dynamic voltage and frequency scaling (DVFS) has been an effective technique that allowed microprocessors to match a predefined power budget. However, as process technology shrinks, DVFS becomes less effective (because of the increasing leakage power) and it is getting closer to a point where DVFS won't be useful at all (when static power exceeds dynamic power). In this paper we propose the use of microarchitectural techniques to accurately match a power constraint while maximizing the energy efficiency of the processor. We predict the processor power consumption at a basic block level, using the consumed power translated into tokens to select between different power-saving micro-architectural techniques. These techniques are orthogonal to DVFS so they can be simultaneously applied. We propose a two-level approach where DVFS acts as a coarse-grained technique to lower the average power while microarchitectural techniques remove all the power spikes efficiently. Experimental results show that the use of power-saving microarchitectural techniques in conjunction with DVFS is up to six times more precise, in terms of total energy consumed (area) over the power budget, than using DVFS alone for matching a predefined power budget. Furthermore, in a near future DVFS will become DFS because lowering the supply voltage will be too expensive in terms of leakage power. At that point, the use of power-saving microarchitectural techniques will become even more energy efficient.

Reliability has become a first-class consideration issue for architects along with performance and energy-efficiency. The increasing scaling technology and subsequent supply voltage reductions are increasing the susceptibility of architectures to soft errors. However, mechanisms to achieve full coverage to errors usually degrade performance in an unacceptable way for the majority of common users. Simultaneous and Redundantly Threaded (SRT) is a fault tolerant architecture in which pairs of threads in a SMT core redundantly execute the same program instructions. In this paper, we study the under-explored architectural support of SRT to reliably execute shared-memory applications. We show how atomic operations induce a serialization point between master and slave threads. This bottleneck has an impact of 34% in execution speed for several parallel scientific benchmarks. We propose an alternative mechanism in which the L1 cache is updated by master's stores before verification reducing the overhead up to 21%. Our approach also outperforms other recent proposals such as DCC with a decrease of 8% in execution speed.

Previous studies have shown that the interconnection network of a Chip-Multiprocessor (CMP) has significant impact on both overall performance and energy consumption. Moreover, wires used in such interconnect can be designed with varying latency, bandwidth and power characteristics. In this work, we present a proposal for performance- and energy-efficient message management in tiled CMPs that combines both address compression with a heterogeneous interconnect. Our proposal consists of applying an address compression scheme that dynamically compresses the addresses within coherence messages allowing for a significant area slack. The arising area can be exploited for wire latency improvement by using a heterogeneous interconnection network comprised of a small set of very-low-latency wires for critical short-messages in addition to baseline wires. Detailed simulations of a 16-core CMP show that our proposal obtains average improvements of 10% in execution time and 38% in the Energy-Delay^2 Product of the interconnect.

As process technology advances toward deep submicron (below 90 nm), static power becomes a new challenge to address for energy-efficient high performance processors, especially for large on-chip array structures such as caches and prediction tables. Value prediction appeared as an effective way of increasing processor performance by overcoming data dependences, but at the risk of becoming a thermal hot spot due to the additional power dissipation. This paper proposes the design of low-leakage value predictors by applying static decay techniques in order to disable unused entries from the prediction tables. We explore decay strategies suited for the three most common Value predictors (STP, FCM and DFCM) studying the particular tradeoffs for these prediction structures. Our mechanism reduces VP leakage energy efficiently without compromising VP accuracy nor processor performance. Results show average leakage energy reductions of 52%, 65% and 75% for the STP, DFCM and FCM value predictors, respectively.

Conventional front-end designs attempt to maximize the number of "in-flight" instructions in the pipeline. However, branch mispredictions cause the processor to fetch useless instructions that are eventually squashed, increasing front-end energy and issue queue utilization and, thus, wasting around 30 percent of the power dissipated by a processor. Furthermore, processor design trends lead to increasing clock frequencies by lengthening the pipeline, which puts more pressure on the branch prediction engine since branches take longer to be resolved. As next-generation high-performance processors become deeply pipelined, the amount of wasted energy due to misspeculated instructions will go up. The aim of this work is to reduce the energy consumption of misspeculated instructions. We propose selective throttling, which triggers different power-aware techniques (fetch throttling, decode throttling, or disabling the selection logic) depending on the branch prediction confidence level. Results show that combining fetch-bandwidth reduction along with select-logic disabling provides the best performance in terms of overall energy reduction and energy-delay product improvement (14 percent and 10 percent, respectively, for a processor with a 22-stage pipeline and 16 percent and 13 percent, respectively, for a processor with a 42-stage pipeline).

This paper proposes a low-energy solution for CAM-based highly associative I-caches using a segmented word-line and a predictor-based instruction fetch mechanism. Not all instructions in a given I-cache fetch are used due to branches. The proposed predictor determines which instructions in a cache access will be used and does not fetch any other instructions. Results show an average I-cache energy savings of 44% over the baseline case and 6% over the segmented case with no negative impact on performance.

With the constant advances in technology that lead to the increasing of the transistor count and processor frequency, power dissipation is becoming one of the major issues in high-performance processors. These processors increase their clock frequency by lengthening the pipeline, which puts more pressure on the branch prediction engine since branches take longer to be resolved. Branch mispredictions are responsible for around 28% of the power dissipated by a typical processor due to the useless activities performed by instructions that are squashed. This work focuses on reducing the power dissipated by mis-speculated instructions. We propose selective throttling as an effective way of triggering different power-aware techniques (fetch throttling, decode throttling or disabling the selection logic). The particular set of techniques applied to each branch is dynamically chosen depending on the branch prediction confidence level. For branches with a low confidence on the prediction, the most aggressive throttling mechanism is used whereas high confidence branch predictions trigger the least aggressive techniques. Results show that combining fetch bandwidth reduction along with select logic disabling provides the best performance both in terms of energy reduction and energy-delay improvement (14% and 9% respectively for 14 stages, and 17% and 12% respectively for 28 stages).

The reasons for performance losses due to conditional branch mispredictions are first studied. Branch misprediction penalties are broken into three categories: pipeline-fill penalty, window-fill penalty, and serialization penalty. The first and third of these produce most of the performance loss, but the second is also significant. Previously proposed dual (or multi) path execution methods attempt to reduce all three penalties, but these methods are also quite complex. Most of the complexity is caused by simultaneously executing instructions from multiple paths. A good

Branch prediction is one of the main hurdles in the roadmap towards deeper pipelines and higher clock frequencies. This work presents a new approach to enhancing current branch predictors: Selective Branch Prediction Reversal. The rationale behind this proposal is the fact that many branch mispredictions can be avoided if branch prediction is selectively reversed. We present a Branch Prediction Reversal Unit (BPRU) that selectively reverses branch predictions by correlating with the predicted values of the branch inputs, in addition to recent control flow. As a case study, we have included the BPRU in an already proposed branch predictor, the Branch Predictor through Value Prediction (BPVP). The effect is a reduction by half in its original misprediction rate. We have also measured the improvement when the BPRU engine is used in a hybrid scheme composed of a BPVP and a gshare predictor. Results using immediate updates show average reductions in misprediction rate ranging from 7% to 14%. Performance evaluation of the proposed BPRU in a 20-stage superscalar processor shows an IPC improvement of up to 9%.