A Survey of Prediction and Classification Techniques in Multicore Processor Systems

In multicore processor systems, being able to accurately predict the future provides new optimization opportunities, which otherwise could not be exploited. For example, an oracle able to predict a certain application\u27s behavior running on a smart phone could direct the power manager to switch to appropriate dynamic voltage and frequency scaling modes that would guarantee minimum levels of desired performance while saving energy consumption and thereby prolonging battery life. Using predictions enables systems to become proactive rather than continue to operate in a reactive manner. This prediction-based proactive approach has become increasingly popular in the design and optimization of integrated circuits and of multicore processor systems. Prediction transforms from simple forecasting to sophisticated machine learning based prediction and classification that learns from existing data, employs data mining, and predicts future behavior. This can be exploited by novel optimization techniques that can span across all layers of the computing stack. In this survey paper, we present a discussion of the most popular techniques on prediction and classification in the general context of computing systems with emphasis on multicore processors. The paper is far from comprehensive, but, it will help the reader interested in employing prediction in optimization of multicore processor systems

Scheduling Analysis from Architectural Models of Embedded Multi-Processor Systems

International audienceAs embedded systems need more and more computing power, many products require hardware platforms based on multiple processors. In case of real-time constrained systems, the use of scheduling analysis tools is mandatory to validate the design choices, and to better use the processing capacity of the system. To this end, this paper presents the extension of the scheduling analysis tool Cheddar to deal with multi-processor schedul- ing. In a Model Driven Engineering approach, useful infor- mation about the scheduling of the application is extracted from a model expressed with an architectural language called AADL. We also define how the AADL model must be writen to express the standard policies for the multi-processor scheduling

Learning-based run-time power and energy management of multi/many-core systems: current and future trends

Multi/Many-core systems are prevalent in several application domains targeting different scales of computing such as embedded and cloud computing. These systems are able to fulfil the everincreasing performance requirements by exploiting their parallel processing capabilities. However, effective power/energy management is required during system operations due to several reasons such as to increase the operational time of battery operated systems, reduce the energy cost of datacenters, and improve thermal efficiency and reliability. This article provides an extensive survey of learning-based run-time power/energy management approaches. The survey includes a taxonomy of the learning-based approaches. These approaches perform design-time and/or run-time power/energy management by employing some learning principles such as reinforcement learning. The survey also highlights the trends followed by the learning-based run-time power management approaches, their upcoming trends and open research challenges

CPU Energy-Aware Parallel Real-Time Scheduling

Both energy-efficiency and real-time performance are critical requirements in many embedded systems applications such as self-driving car, robotic system, disaster response, and security/safety control. These systems entail a myriad of real-time tasks, where each task itself is a parallel task that can utilize multiple computing units at the same time. Driven by the increasing demand for parallel tasks, multi-core embedded processors are inevitably evolving to many-core. Existing work on real-time parallel tasks mostly focused on real-time scheduling without addressing energy consumption. In this paper, we address hard real-time scheduling of parallel tasks while minimizing their CPU energy consumption on multicore embedded systems. Each task is represented as a directed acyclic graph (DAG) with nodes indicating different threads of execution and edges indicating their dependencies. Our technique is to determine the execution speeds of the nodes of the DAGs to minimize the overall energy consumption while meeting all task deadlines. It incorporates a frequency optimization engine and the dynamic voltage and frequency scaling (DVFS) scheme into the classical real-time scheduling policies (both federated and global) and makes them energy-aware. The contributions of this paper thus include the first energy-aware online federated scheduling and also the first energy-aware global scheduling of DAGs. Evaluation using synthetic workload through simulation shows that our energy-aware real-time scheduling policies can achieve up to 68% energy-saving compared to classical (energy-unaware) policies. We have also performed a proof of concept system evaluation using physical hardware demonstrating the energy efficiency through our proposed approach

Performance of a parallel code for the Euler equations on hypercube computers

The performance of hypercubes were evaluated on a computational fluid dynamics problem and the parallel environment issues were considered that must be addressed, such as algorithm changes, implementation choices, programming effort, and programming environment. The evaluation focuses on a widely used fluid dynamics code, FLO52, which solves the two dimensional steady Euler equations describing flow around the airfoil. The code development experience is described, including interacting with the operating system, utilizing the message-passing communication system, and code modifications necessary to increase parallel efficiency. Results from two hypercube parallel computers (a 16-node iPSC/2, and a 512-node NCUBE/ten) are discussed and compared. In addition, a mathematical model of the execution time was developed as a function of several machine and algorithm parameters. This model accurately predicts the actual run times obtained and is used to explore the performance of the code in interesting but yet physically realizable regions of the parameter space. Based on this model, predictions about future hypercubes are made

The Thermal-Constrained Real-Time Systems Design on Multi-Core Platforms -- An Analytical Approach

Over the past decades, the shrinking transistor size enabled more transistors to be integrated into an IC chip, to achieve higher and higher computing performances. However, the semiconductor industry is now reaching a saturation point of Moore’s Law largely due to soaring power consumption and heat dissipation, among other factors. High chip temperature not only significantly increases packing/cooling cost, degrades system performance and reliability, but also increases the energy consumption and even damages the chip permanently. Although designing 2D and even 3D multi-core processors helps to lower the power/thermal barrier for single-core architectures by exploring the thread/process level parallelism, the higher power density and longer heat removal path has made the thermal problem substantially more challenging, surpassing the heat dissipation capability of traditional cooling mechanisms such as cooling fan, heat sink, heat spread, etc., in the design of new generations of computing systems. As a result, dynamic thermal management (DTM), i.e. to control the thermal behavior by dynamically varying computing performance and workload allocation on an IC chip, has been well-recognized as an effective strategy to deal with the thermal challenges. Over the past decades, the shrinking transistor size, benefited from the advancement of IC technology, enabled more transistors to be integrated into an IC chip, to achieve higher and higher computing performances. However, the semiconductor industry is now reaching a saturation point of Moore’s Law largely due to soaring power consumption and heat dissipation, among other factors. High chip temperature not only significantly increases packing/cooling cost, degrades system performance and reliability, but also increases the energy consumption and even damages the chip permanently. Although designing 2D and even 3D multi-core processors helps to lower the power/thermal barrier for single-core architectures by exploring the thread/process level parallelism, the higher power density and longer heat removal path has made the thermal problem substantially more challenging, surpassing the heat dissipation capability of traditional cooling mechanisms such as cooling fan, heat sink, heat spread, etc., in the design of new generations of computing systems. As a result, dynamic thermal management (DTM), i.e. to control the thermal behavior by dynamically varying computing performance and workload allocation on an IC chip, has been well-recognized as an effective strategy to deal with the thermal challenges. Different from many existing DTM heuristics that are based on simple intuitions, we seek to address the thermal problems through a rigorous analytical approach, to achieve the high predictability requirement in real-time system design. In this regard, we have made a number of important contributions. First, we develop a series of lemmas and theorems that are general enough to uncover the fundamental principles and characteristics with regard to the thermal model, peak temperature identification and peak temperature reduction, which are key to thermal-constrained real-time computer system design. Second, we develop a design-time frequency and voltage oscillating approach on multi-core platforms, which can greatly enhance the system throughput and its service capacity. Third, different from the traditional workload balancing approach, we develop a thermal-balancing approach that can substantially improve the energy efficiency and task partitioning feasibility, especially when the system utilization is high or with a tight temperature constraint. The significance of our research is that, not only can our proposed algorithms on throughput maximization and energy conservation outperform existing work significantly as demonstrated in our extensive experimental results, the theoretical results in our research are very general and can greatly benefit other thermal-related research

Solving Lattice QCD systems of equations using mixed precision solvers on GPUs

Modern graphics hardware is designed for highly parallel numerical tasks and promises significant cost and performance benefits for many scientific applications. One such application is lattice quantum chromodyamics (lattice QCD), where the main computational challenge is to efficiently solve the discretized Dirac equation in the presence of an SU(3) gauge field. Using NVIDIA's CUDA platform we have implemented a Wilson-Dirac sparse matrix-vector product that performs at up to 40 Gflops, 135 Gflops and 212 Gflops for double, single and half precision respectively on NVIDIA's GeForce GTX 280 GPU. We have developed a new mixed precision approach for Krylov solvers using reliable updates which allows for full double precision accuracy while using only single or half precision arithmetic for the bulk of the computation. The resulting BiCGstab and CG solvers run in excess of 100 Gflops and, in terms of iterations until convergence, perform better than the usual defect-correction approach for mixed precision.Comment: 30 pages, 7 figure

Power analysis with variable traffic loads for next generation interconnection networks

Power consumption is the most important factor for the consideration of next generation supercomputers. In addition, the requirement of power usages can be even scaled up to more than 300MW (which is nearly equal to the one nuclear power plant) with the conventional networks. On the other hand, hierarchical interconnection networks can be a possible solution to those issues. 3D-TTN is a hierarchical interconnection network where lowest level is configured as the 3Dtorus network, following the 2Dtorus network at the higher-level networks. The main focus for this paper is the power analysis with variable traffic load along with the fault tolerance, cost, packing density and message traffic density of 3D-TTN comparing against various other networks. In our early research, 3D-TTN has achieved near about 21% better diameter performance, 12% better average distance performance and eventually required about 32.48% less router power usage at the lowest level than the 5Dtorus network for 1% traffic load. This paper shows the power comparison with the router and link power rather than considering the router power only. Our analysis shows that 3DTTN will require about 39.96% less router and link power than the 5Dtorus network for 10% traffic. With 30% traffic load, 3DTTN will require about 38.42% less power than the 5Dtorus network for the on-chip network. Even considering some topological parameters, 3D-TTN could also achieve some desirable performance by comparing other networks

A Multithreaded Soft Processor for SoPC Area Reduction

The growth in size and performance of Field Programmable Gate Arrays (FPGAs) has compelled System-on-a-Programmable-Chip (SoPC) designers to use soft proces-sors for controlling systems with large numbers of intellec-tual property (IP) blocks. Soft processors control IP blocks, which are accessed by the processor either as peripheral de-vices or/and by using custom instructions (CIs). In large systems, chip multiprocessors (CMPs) are used to execute many programs concurrently. When these programs require the use of the same IP blocks which are accessed as periph-eral devices, they may have to stall waiting for their turn. In the case of CIs, the FPGA logic blocks that implement the CIs may have to be replicated for each processor. In both of these cases FPGA area is wasted, either by idle soft processors or the replication of CI logic blocks. This paper presents a multithreaded (MT) soft processor for area reduction in SoPC implementations. An MT proces-sor allows multiple programs to access the same IP without the need for the logic replication or the replication of whole processors. We first designed a single-threaded processor that is instruction-set compatible to Altera’s Nios II soft processor. Our processor is approximately the same size as the Nios II Economy version, with equivalent performance. We augmented our processor to have 4-way interleaved mul-tithreading capabilities. This paper compares the area us-age and performance of the MT processor versus two CMP systems, using Altera’s and our single-threaded processors, separately. Our results show that we can achieve an area savings of about 45 % for the processor itself, in addition to the area savings due to not replicating CI logic blocks. 1

Switch-based packing technique to reduce traffic and latency in token coherence

Token Coherence is a cache coherence protocol able to simultaneously capture the best attributes of traditional protocols: low latency and scalability. However it may lose these desired features when (1) several nodes contend for the same memory block and (2) nodes write highly-shared blocks. The first situation leads to the issue of simultaneous broadcast requests which threaten the protocol scalability. The second situation results in a burst of token responses directed to the writer, which turn it into a bottleneck and increase the latency. To address these problems, we propose a switch-based packing technique able to encapsulate several messages (while in transit) into just one. Its application to the simultaneous broadcasts significantly reduces their bandwidth requirements (up to 45%). Its application to token responses lowers their transmission latency (by 70%). Thus, the packing technique decreases both the latency and coherence traffic, thereby improving system performance (about 15% of reduction in runtime). © 2011 Elsevier Inc. All rights reserved.This work was partially supported by the Spanish MEC and MICINN, as well as European Commission FEDER funds, under Grants CSD2006-00046 and TIN2009-14475-C04-01.Cuesta Sáez, BA.; Robles Martínez, A.; Duato Marín, JF. (2012). Switch-based packing technique to reduce traffic and latency in token coherence. Journal of Parallel and Distributed Computing. 72(3):409-423. https://doi.org/10.1016/j.jpdc.2011.11.010S40942372