Design of multimedia processor based on metric computation

Media-processing applications, such as signal processing, 2D and 3D graphics rendering, and image compression, are the dominant workloads in many embedded systems today. The real-time constraints of those media applications have taxing demands on today's processor performances with low cost, low power and reduced design delay. To satisfy those challenges, a fast and efficient strategy consists in upgrading a low cost general purpose processor core. This approach is based on the personalization of a general RISC processor core according the target multimedia application requirements. Thus, if the extra cost is justified, the general purpose processor GPP core can be enforced with instruction level coprocessors, coarse grain dedicated hardware, ad hoc memories or new GPP cores. In this way the final design solution is tailored to the application requirements. The proposed approach is based on three main steps: the first one is the analysis of the targeted application using efficient metrics. The second step is the selection of the appropriate architecture template according to the first step results and recommendations. The third step is the architecture generation. This approach is experimented using various image and video algorithms showing its feasibility

PowerPack: Energy Profiling and Analysis of High-Performance Systems and Applications

Energy efficiency is a major concern in modern high-performance computing system design. In the past few years, there has been mounting evidence that power usage limits system scale and computing density, and thus, ultimately system performance. However, despite the impact of power and energy on the computer systems community, few studies provide insight to where and how power is consumed on high-performance systems and applications. In previous work, we designed a framework called PowerPack that was the first tool to isolate the power consumption of devices including disks, memory, NICs, and processors in a high-performance cluster and correlate these measurements to application functions. In this work, we extend our framework to support systems with multicore, multiprocessor-based nodes, and then provide in-depth analyses of the energy consumption of parallel applications on clusters of these systems. These analyses include the impacts of chip multiprocessing on power and energy efficiency, and its interaction with application executions. In addition, we use PowerPack to study the power dynamics and energy efficiencies of dynamic voltage and frequency scaling (DVFS) techniques on clusters. Our experiments reveal conclusively how intelligent DVFS scheduling can enhance system energy efficiency while maintaining performance

Architecture-Aware Configuration and Scheduling of Matrix Multiplication on Asymmetric Multicore Processors

Asymmetric multicore processors (AMPs) have recently emerged as an appealing technology for severely energy-constrained environments, especially in mobile appliances where heterogeneity in applications is mainstream. In addition, given the growing interest for low-power high performance computing, this type of architectures is also being investigated as a means to improve the throughput-per-Watt of complex scientific applications. In this paper, we design and embed several architecture-aware optimizations into a multi-threaded general matrix multiplication (gemm), a key operation of the BLAS, in order to obtain a high performance implementation for ARM big.LITTLE AMPs. Our solution is based on the reference implementation of gemm in the BLIS library, and integrates a cache-aware configuration as well as asymmetric--static and dynamic scheduling strategies that carefully tune and distribute the operation's micro-kernels among the big and LITTLE cores of the target processor. The experimental results on a Samsung Exynos 5422, a system-on-chip with ARM Cortex-A15 and Cortex-A7 clusters that implements the big.LITTLE model, expose that our cache-aware versions of gemm with asymmetric scheduling attain important gains in performance with respect to its architecture-oblivious counterparts while exploiting all the resources of the AMP to deliver considerable energy efficiency

Dynamic Processor Reconfiguration for Power, Performance and Reliability Management

Technology advancements allowed more transistors to be packed in a smaller area, while the improved performance helped in achieving higher clock frequencies. This, unfortunately led to a power density problem, forcing processor industry to lower the clock frequency and integrate multiple cores on the same die. Depending on core characteristics, the multiple cores in the die could be symmetric or asymmetric. Asymmetric multi-core processors (AMPs) have been proposed as an alternative to symmetric multi-cores to improve power efficiency. AMPs comprise of cores that implement the same ISA, but differ in performance and power characteristics due to varying sizes of micro-architectural resources. As the computational bottleneck of a workload shifts from one resource to another during its course of execution, reassigning it to another core (where it runs more efficiently), can improve the overall power efficiency. Thus achieving high power efficiency in AMPs requires (i) a diverse set of cores that are optimized for various program phases, (ii) runtime analysis to determine the best core to run on, and (iii) low overhead of re-assigning a thread to a different core type. Decisions to swap threads between AMPs are made at coarse grain granularity of millions of instructions, to mitigate the impact of thread migration overhead. But the computational needs of the program rapidly change during the course of its execution. The best core configuration for an application such that, both power consumption and performance are optimized, changes over time rapidly at fine granularity of thousands of instructions. This dissertation explores ways to design core micro-architecture such that high power efficiency could be achieved, if switching overhead could be lowered, enabling fine grain switching. To take advantage of power saving opportunities at fine grain granularity, this thesis explores reconfigurable/morphable architectures where core resources are reconfigured on demand to suit the needs of the executing application. At first, we explore reconfigurable architectures consisting of two kinds of cores: out-of-order (OOO) big cores and in-order (InO) small cores. The big cores provide higher performance while the small cores are more power efficient. In this proposed architecture, OOO core reconfigures into InO core at run time. Our proposed online management scheme decides to switch between these core types such that we obtain significant power benefits without impacting performance. We also observe that, resource requirements of applications can be quite diverse and consequently, resource bottlenecks or excesses can vary considerably. Thus, reconfiguration between just two core modes may not fully exploit power and performance improvement opportunities. We therefore, explore reconfigurable architectures consisting of diverse core types that not limited to big and little cores. A single core can reconfigure into multiple core modes where each mode has unique power and performance characteristics. Workload performance on a particular core mode depends on a large set of processor resources. Some workloads are highly memory intensive, some exhibit large instruction dependency, some experience high rates of branch mis-prediction, while other workloads exhibit large exploitable instruction level parallelism. A diverse set of core modes is needed, that could address shifting resource needs during various program phases of an application. Different trade-offs in power and performance could be achieved by reducing or expanding the size of various resource. Trade-offs for each core mode are also affected by operating voltage and frequency. We therefore, propose joint core resource resizing with dynamic voltage and frequency scaling (DVFS), which is important for applications whose performance is sensitive to changes in frequency. Thus, at fine granularity, the core should adapt to varying instruction window sizes, execution bandwidth and frequency to meet the demands of the workload at run-time to improve power efficiency. Many current processors employ DVFS aggressively to improve power efficiency and maximize performance. This dissertation studies the tradeoff in power efficiency in using fine grain DVFS and reconfigurable architectures mentioned above.We also explore another important problem due to continued scaling of devices which results in higher vulnerability to soft-errors. We consider dynamic core reconfiguration from the perspectives of both power efficiency and vulnerability to soft-errors. An online management scheme is proposed such that core reconfiguration upon a thread switch not only improves power efficiency but also does not increase the vulnerability to soft errors. In summary, we propose in this thesis several solutions for improving power efficiency by integrating heterogeneity within the core. We also address how popular power reduction techniques like DVFS are comparable to our approach. Finally, we address reliability challenges along with improving power efficiency

EPICURE: A partitioning and co-design framework for reconfigurable computing

This paper presents a new design methodology able to bridge the gap between an abstract specification and a heterogeneous reconfigurable architecture. The EPICURE contribution is the result of a joint study on abstraction/refinement methods and a smart reconfigurable architecture within the formal Esterel design tools suite. The original points of this work are: (i) a generic HW/SW interface model, (ii) a specification methodology that handles the control, and includes efficient verification and HW/SW synthesis capabilities, (iii) a method for parallelism exploration based on abstract resources/performance estimation expressed in terms of area/delay tradeoffs, (iv) a HW/SW partitioning approach that refines the specification into explicit HW configurations and the associated SW control. The EPICURE framework shows how a cooperation of complementary methodologies and CAD tools associated with a relevant architecture can signficantly improve the designer productivity, especially in the context of reconfigurable architectures

Percolation scheduling with resource constraints

This paper presents a new approach to resource-constrained compiler extraction of fine-grain parallelism, targeted towards VLIW supercomputers, and in particular, the IBM VLIW (Very Large Instruction Word) processor. The algorithms described integrate resource limitations into Percolation Scheduling—a global parallelization technique—to deal with resource constraints, without sacrificing the generality and completeness of Percolation Scheduling in the process. This is in sharp contrast with previous approaches which either applied only to conditional-free code, or drastically limited the parallelization process by imposing relatively local heuristic resource constraints early in the scheduling process

Scheduling strategies for parallel patterns on heterogeneous architectures

To help shrink the programmability-performance efficiency gap, we discuss that adaptive runtime systems can be used to facilitate the management of heterogeneous architectures. A runtime system can provide a significant performance boost while reducing the energy consumption, because it is aware of processors’ architectures and application’s requirements. We analyse how applications map onto hardware by inspecting built-in processor counters, and therefore build models to describe the observed behaviour. In this thesis, we discuss how parallel patterns, such as parallel for loops and pipelines, can be decomposed and efficiently executed on heterogeneous plat- forms. We propose several scheduling strategies aiming at reducing execution time and energy consumption. We demonstrate how applications can be run faster by mapping the application level parallelism onto the hardware process- ing units that best fit the application requirements, and by selecting the right task size. First, we devise a load balancing technique, that targets heterogeneous CPU and multi-GPU architectures. It monitors the relative speed of each processing unit, and distributes the remaining workload based on these relative speeds. By making all processing units to finish at same time, we avoid unnecessary waits between processors. Along with this load balancing technique, we propose a performance-sensitive partitioner that adapts the amount of computation offloaded to the accelerator for better performance and utilisation. We also present an accurate performance model for streaming applications, such as face recognition or object tracking. This model targets pipelined applications, as a series of stages, and performs a scalability analysis of each stage by using coarse and medium grain parallelism. Additionally, it also considers executing the stage on the GPU or not. By applying the model, we always find the best pipeline configuration among all possible, and get substantial performance and energy savings. All experiments in this thesis have been performed by using state-of-the-art hardware accelerators and benchmarks of the field of HPC. Specifically, we use the Rodinia and SHOC benchmark suites, for the evaluation of the parallel for partitioner. Moreover, we use the the ViVid application, along with tracking and SRAD applications from Rodinia Benchmark Suite, all of them are good candidates of vision applications. Finally, we rely on Intel Threading Building Blocks, the core engine of our schedulers; the Intel OpenCL SDK and CUDA SDK to offload computations to the GPU accelerators and Intel PCM library to monitor energy consumption and cache memory metrics.During the last decade, power consumption and energy efficiency have become key aspects in processor design. Nowadays, the power consumption is the principal limitation for further scaling of chip multiprocessors design (CMPs). In general, the research community agrees that current chip multiprocessor technology trends will not scale performance without an increase of power budget. Hardware design innovations as the recent Heterogeneous Architectures and Near Threshold Computing are needed to cope with the performance-power barrier. As a result of this, there has been a shift away from chip multiprocessors to heterogeneous processor architectures. Recently, we have witnessed an explosion in the availability of this kind of architectures. Many hardware vendors have released a number of heterogeneous processors to overcome the aforementioned limitations. However, software also requires changes to allow further performance scaling on these architectures. With the advent of heterogeneous architectures, hardware manufactures have impose the burden of explicit accelerator management on software developers. In general, programmers are used to sequential programming, but writing high-performance programs for heterogeneous architectures is a complex task. Programming for this kind of platforms requires the understanding of new hardware concepts, orchestration of different parallelism levels, the explicit management of different memory spaces and synchronisations between processing units, and finally the usage of low-level programming models such as OpenCL or CUDA. Moreover, heterogeneous architectures suffer from performance portability, as one program can exhibit unequal performance on different devices