An Approach for Implementing Efficient Superscalar CISC Processors

Effect of gas flow-field design in the bipolar/end plates on the steady and transient state performance of polymer electrolyte membrane fuel cells

High performance processors based on pipeline processing play an important role in scientific computation. We have proposed a hybrid pipeline architecture named Jetpipeline in our former work. The concept of Jetpipeline comes from the integration of superscalar, VLIW and vector architectures. Jetpipeline has multiple instruction pipelines, which execute multiple instructions like superscalar architectures. Instructions to be executed simultaneously are statically scheduled by a compiler like VLIW architectures. Therefore, parallelism derivation and instruction scheduling are very important for Jetpipeline. Software pipelining is one of the well-known techniques to achieve high throughput when processing loop programs. In this paper, we propose software pipelining for Jetpipeline. Firstly, the overview of the Jetpipeline architecture is described. Then the banked register configuration of Jetpipeline for reducing hardware complexity and supporting software pipelining is presented. Finally, the effectiveness of software pipelining for Jetpipeline is discussed by simulation. >

The performance of microprocessors has increased exponentially for over 35 years. However, process technology challenges, chip power constraints, and difficulty in extracting instruction-level parallelism are conspiring to limit the performance of future individual processors. To address these limits, the computer industry has embraced chip multiprocessing (CMP), predominately in the form of multiple high-performance superscalar processors on the same die. We explore the trade-off between building CMPs from a few high-performance cores or building CMPs from a large number of lower-performance cores and argue that CMPs built from a larger number of lower-performance cores can provide better performance and performance/Watt on many commercial workloads. We examine two multi-threaded CMPs built using a large number of processor cores: Sun's Niagara and Niagara 2 processors. We also explore the programming issues for CMPs with large number of threads. The programming model for these CMPs is similar to the widely used programming model for symmetric multiprocessors (SMPs), but the greatly reduced costs associated with communication of data through the on-chip shared secondary cache allows for more fine-grain parallelism to be effectively exploited by the CMR Finally, we present performance comparisons between Sun's Niagara and more conventional dual-core processors built from large superscalar processor cores. For several key server workloads, Niagara shows significant performance and even more significant performance/Watt advantages over the CMPs built from traditional superscalar processors.

A superscalar processor with three execution units is described in details in this paper. The execution units are implemented using reprogrammable field-programmable gate array (FPGA) chips, allowing the user to configure them to best fit the application. Each instruction, composed of 128 bits, directly controls all the processor resources, in a horizontal microprogramming way.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

UItraSpare I is a second-generation superscalar processor. It is a high performance, highly integrated, four issue superscalar processor based on the Spare Version 9 64-bit RISC architecture. We have extended the core instruction set to include graphics instructions that provide the most common operations related to two dimensional image processing; two- and three-dimensional graphics and image compression algorithms; and parallel operations on pixel data with 8-, 16-, and 32-bit components. Additional, new memory access instructions support the very high bandwidth requirements typical of graphics and multimedia applications.

Value prediction improves instruction level parallelism in superscalar processors by breaking true data dependencies. Although this technique can significantly improve overall performance, most of the state-of-the-art value prediction approaches require high hardware cost, which is the main obstacle for its wide adoption in current processors. To tackle this issue, we revisit load value prediction as an efficient alternative to the classical approaches that predict all instructions. By speculating only on loads, the pressure over shared resources (e.g., the Physical Register File) and the predictor size can be substantially reduced (e.g., more than 90% reduction compared to recent works). We observe that existing value predictors cannot achieve very high performance when speculating only on load instructions. To solve this problem, we propose a new, accurate and low-cost mechanism for predicting the values of load instructions: the Address-first Value-next Predictor with Value Prefetching (AVPP). The key idea of our predictor is to predict the load address first (which, we find, is much more predictable than the value) and to use a small non-speculative Value Table (VT)—indexed by the predicted address—to predict the value next. To increase the coverage of AVPP, we aim to increase the hit rate of the VT by predicting also the load address of a future instance of the same load instruction and prefetching its value in the VT. We show that AVPP is relatively easy to implement, requiring only 2.5% of the area of a 32KB L1 data cache. We compare our mechanism with five state-of-the-art value prediction techniques, evaluated within the context of load value prediction, in a relatively narrow out-of-order processor. On average, our AVPP predictor achieves 11.2% speedup and 3.7% of energy savings over the baseline processor, outperforming all the state-of-the-art predictors in 16 of the 23 benchmarks we evaluate. We evaluate AVPP implemented together with different prefetching techniques, showing additive performance gains (20% average speedup). In addition, we propose a new taxonomy to classify different value predictor policies regarding predictor update, predictor availability, and in-flight pending updates. We evaluate these policies in detail.

The potential performance of superscalar processors can be exploited only when processor is fed with sufficient instruction bandwidth. The front-end units, the Instruction Stream Buffer (ISB) and the fetcher, are the key elements for achieving this goal. Current ISBs could not support instruction streaming beyond a basic block. In x86 processors, the split-line instruction problem worsens this situation. In this paper, we proposed a basic blocks reassembling ISB. By cooperating with the proposed Line-Weighted Branch Target Buffer (LWBTB), the proposed ISB can predict upcoming branch information and reassemble current cache line together with the other cache line containing instructions for the next basic block. Therefore, the fetcher could fetch more instructions in a cycle with the assistance of the proposed ISB. Simulation results show that the cache line size over 64 bytes has a good chance to let two basic blocks present in a reassembled cache line and the fetch efficiency is about 90% as the fetch capacity is under 6.

Dynamic voltage and frequency scaling (DVFS) is commonly employed on modern superscalar processors to reduce energy when peak performance is not needed or allowed. As technology scales, the effectiveness of DVFS is limited by the shrinking viable supply voltage range. This work proposes dynamic core scaling (DCS) to extend performance-energy tradeoff capabilities in superscalar processors. DCS ensures that programs run at a given percentage of their maximum speed and, at the same time, minimizes energy consumption by dynamically adjusting the active superscalar datapath resources. Evaluations using an 8-way superscalar processor implemented on 45nm circuit infrastructure show that DCS is more effective in performance-energy tradeoffs than DVFS at the high performance end. When used together with DVFS, DCS saves an additional 20% of a full-size core's energy on average. At the minimum operating voltage, DVFS stops reducing energy, while DCS is still able to achieve an average of 46% further energy reduction.

Model predictive control (MPC) has attracted widespread attention in both academic and industry communities due to its merits of intuitive concept, quick dynamic response, multi-variable control, ability to handle various nonlinear constraints, and so on. It is considered a powerful alternative to field oriented control (FOC) and direct torque control (DTC) in high performance AC motor drives. Compared to FOC, MPC eliminates the use of internal current control loops and modulation block, hence featuring very quick dynamic response. Compared to DTC, MPC uses a cost function rather than a heuristic switching table to select the best voltage vector, producing better steady state performance. In spite of the merits above, MPC also presents some drawbacks such as high computational burden, nontrivial weighting factor tuning, high sampling frequency, variable switching frequency, model/parameter dependence and relatively high steady ripples in torque and stator flux. This paper presents the state of the art of MPC in high performance induction motor (IM) drives, and in particular the progress on solving the drawbacks of conventional MPC. Finally, one of the improved MPC is compared to FOC to validate its superiority. It is shown that the improved MPC has great potential in the future high performance AC motor drives.

A machine's performance is the product of its IPC (Instructions Per Cycle) and clock frequency. Recently, S. Palacharla et al. (1997) warned that the dynamic instruction scheduling logic for current machines performs an atomic operation. Either you sacrifice IPC by pipelining this logic, thereby eliminating its ability to execute dependent instructions in consecutive cycles. Or you sacrifice clock frequency by not pipelining it, performing this atomic operation in a single long cycle. Both alternatives are unacceptable for high performance. The paper offers a third, acceptable, alternative: pipelined scheduling with speculative wakeup. This technique pipelines the scheduling logic without eliminating its ability to execute dependent instructions in consecutive cycles. With this technique, you sacrifice little IPC, and no clock frequency. Our results show that on the SPECint95 benchmarks, a machine using this technique has an average IPC that is 13% greater than the IPC of a baseline machine that pipelines the scheduling logic but sacrifices the ability to execute dependent instructions in consecutive cycles, and within 2% of the IPC of a conventional machine that uses single cycle scheduling logic.

Direct torque control of inverter-fed induction motor allows high dynamic performance by means of very simple control schemes. In this paper various direct torque control methodologies (classical DTC, DSVM-DTC and SVM-DTC) have been analysed and compared in order to evaluate the influence of the motor operating condition on steady state performances. A particular emphasis on stator current distortion and on torque ripple has been made. Having a larger number of available voltage vector, DSVM-DTC presents smaller torque ripple than classical-DTC and shows the same flux and stator current performances. SVM-DTC achieves the best performances, but it uses more complex scheme and it depends on motor parameters. Experimental tests have been carried out in order to validate the proposed analysis.

This paper focus on a novel operation of a brushless dc (BLDC) motor fed by a proportional integral (PI) controlled buck--boost converter supplemented with a battery to provide the required power to drive the BLDC motor. The operational characteristics of the proposed BLDC motor drive system for constant as well as step changes in dc link voltage of a front end converter controlled by a Xilinx System Generator (XSG) based PI controller for two quadrant operations are derived. Thus a field programmable gate array (FPGA) based PI controller manages the energy flow through the battery and the front end converter. Moreover, speed to voltage conversion logic, made to control the BLDC motor through the PI controller, improves the performance and gives optimum control under the unstable driving situation or varying load condition when the complete system becomes a subject of application to electric vehicles (EVs) and hybrid electric vehicles (HEVs). The dual closed loop control implemented for end to end speed control of the proposed drive system facilitates the system with high accuracy integrated with excellent dynamic and steady state performance. In this paper, the proposed controller was designed for a 5 kW/480 V BLDC motor drive system. The feasibility of the proposed dual loop control topology for the BLDC motor drive system is validated and verified with extensive dynamic simulation in MATLAB/SIMULINK and XSG environment.

In accordance with the randomness and intermittent characteristics of wind power, the static synchronous compensator (STATCOM) is more competent and competitive for reactive power compensation in large scale wind farm. An average state model for the VSC-STATCOM is derived, according to the wind farm reactive power compensation characteristics, a novel decoupling control strategy of active power and reactive power is proposed. The model and controller are suitable for the reactive power compensation and converter operation state control. A PSCAD/EMTDC simulation model is built and the simulated results are presented. The simulated results show that the proposed STATCOM control strategy provides high performances in both steady state and dynamics.

Network-on-Chip (NoC)-based multiprocessor system-on-chips (MPSoCs) are becoming the de-facto computing platform for computationally intensive real-time applications in the embedded systems due to their high performance, exceptional quality-of-service (QoS) and energy efficiency over superscalar uniprocessor architectures. Energy saving is important in the embedded system because it reduces the operating cost while prolongs lifetime and improves the reliability of the system. In this paper, contention-aware energy efficient static mapping using NoC-based heterogeneous MPSoC for real-time tasks with an individual deadline and precedence constraints is investigated. Unlike other schemes task ordering, mapping, and voltage assignment are performed in an integrated manner to minimize the processing energy while explicitly reduce contention between the communications and communication energy. Furthermore, both dynamic voltage and frequency scaling and dynamic power management are used for energy consumption optimization. The developed contention-aware integrated task mapping and voltage assignment (CITM-VA) static energy management scheme performs tasks ordering using earliest latest finish time first (ELFTF) strategy that assigns priorities to the tasks having shorter latest finish time (LFT) over the tasks with longer LFT. It remaps every task to a processor and/or discrete voltage level that reduces processing energy consumption. Similarly, the communication energy is minimized by assigning discrete voltage levels to the NoC links. Further, total energy efficiency is achieved by putting the processor into a low-power state when feasible. Moreover, this approach resolves the contention between communications that traverse the same link by allocating links to communications with higher priority. The results obtained through extensive simulations of real-world benchmarks demonstrate that CITM-VA approach outperforms state-of-the-art technique and achieves an average ~30% total energy improvement. Additionally, it maintains high QoS and robustness for real-time applications.

During recent years, big data explosion and the increase in main memory capacity, on the one hand, and the need for faster data processing, on the other hand, have caused the development of various in-memory processing tools to manage and analyze data. Engaging the speed of the main memory and advantaging data locality, these tools can process a large amount of data with high performance. Apache Ignite, as a distributed in-memory platform, can process massive volumes of data in parallel. Currently, this platform is CPU-based and does not utilize the GPU’s processing resources. To address this concern, we introduce Ignite-GPU that uses the GPU’s massively parallel processing power. Ignite-GPU handles a number of challenges in integrating GPUs into Ignite and utilizes the GPU’s available resources. We have also identified and eliminated time-consuming overheads and used various GPU-specific optimization techniques to improve overall performance. Eventually, we have evaluated Ignite-GPU with the Genetic Algorithm, as a representative of data and compute-intensive algorithms, and gained more than thousands of times speedup in comparison with its CPU version.