scholarly journals Sense- boarding algorithm in superscalar, scalable multicore and data-inspired processors.

2019 ◽  
Author(s):  
Anil Kumar Bheemaiah
Keyword(s):  
Scheduling Algorithm ◽  
Speculative Execution ◽  
Instruction Level Parallelism ◽  
Sensitivity Index ◽  
Data Generation ◽  
Data Dependencies ◽  
Sensitivity Indices ◽  
Data Speculation ◽  
Cuda Architecture ◽  
Level Parallelism

A new algorithm of data dependencies and ILP is defined with the sense index of a thread in true-parallelism(™), from the definitions of Quasi-Parallelism, which is the sensitivity and sense indices defined for true scalability between single/multi-cores. The application to the CUDA architecture is delineated in formal architectural definitions. Keywords: CUDA architectures, superscalar, ILP, data prediction, sense sensitivity index. What:Out of order processing in a pipeline, can be optimized with the sense-boarding processor. In this single to multi-core scalable architecture, the processor is thread-centric with sleeping and active threads. Sleeping threads have a sense() function associated with them. Unlike their human counterparts, snoring is a useful feature that helps keep sensitive threads awake and running. sense-boarding is a scheduling algorithm that tracks the sensitivity indices of threads to snoring and helps schedule threads with dependency relationships for out of order execution.How:sense boarding is a board based dependency for instruction-level parallelism in multi-thread vector processing in out of order single-core/multicore symmetries.Inter thread dependencies of data are marked in a board data-structure by maps to define sensitivity and sense indices, sense functionality is useful in the case of dependencies, resource waiting and speculative execution or in data generation and prediction. sense determines the relationship in instruction-level parallelism, to sensitive out of order and data speculation. The application to the CUDA architecture for stream processing in GPUs is also mentioned.Algorithms are:Instruction level parallelism in sense-sensitivity index metrics:Data speculation , dirty caches, parallel pipeline algorithms.Scalability in single core/ multi core implementations.CUDA multi core architectures for stream speculation and instruction level parallelism.Why: Sleep is rest, and sense a measure of thread parallel-ness. While threads sleep for the right time, the awake ones perform in quasi-parallelism as HPC. Asynchronous with Lamport clocks.

scholarly journals Microarchitectural Characterization on a Mobile Workload

2021 ◽  
Vol 11 (3) ◽  
pp. 1225
Author(s):  
Woohyong Lee ◽  
Jiyoung Lee ◽  
Bo Kyung Park ◽  
R. Young Chul Kim
Keyword(s):  
Performance Monitoring ◽  
Performance Metrics ◽  
Performance Comparison ◽  
Instruction Level Parallelism ◽  
Data Set ◽  
Performance Events ◽  
Hardware Performance Counters ◽  
On Chip ◽  
The Comparative Study ◽  
Level Parallelism

Geekbench is one of the most referenced cross-platform benchmarks in the mobile world. Most of its workloads are synthetic but some of them aim to simulate real-world behavior. In the mobile world, its microarchitectural behavior has been reported rarely since the hardware profiling features are limited to the public. As a popular mobile performance workload, it is hard to find Geekbench’s microarchitecture characteristics in mobile devices. In this paper, a thorough experimental study of Geekbench performance characterization is reported with detailed performance metrics. This study also identifies mobile system on chip (SoC) microarchitecture impacts, such as the cache subsystem, instruction-level parallelism, and branch performance. After the study, we could understand the bottleneck of workloads, especially in the cache sub-system. This means that the change of data set size directly impacts performance score significantly in some systems and will ruin the fairness of the CPU benchmark. In the experiment, Samsung’s Exynos9820-based platform was used as the tested device with Android Native Development Kit (NDK) built binaries. The Exynos9820 is a superscalar processor capable of dual issuing some instructions. To help performance analysis, we enable the capability to collect performance events with performance monitoring unit (PMU) registers. The PMU is a set of hardware performance counters which are built into microprocessors to store the counts of hardware-related activities. Throughout the experiment, functional and microarchitectural performance profiles were fully studied. This paper describes the details of the mobile performance studies above. In our experiment, the ARM DS5 tool was used for collecting runtime PMU profiles including OS-level performance data. After the comparative study is completed, users will understand more about the mobile architecture behavior, and this will help to evaluate which benchmark is preferable for fair performance comparison.

scholarly journals UltraSynth: Insights of a CGRA Integration into a Control Engineering Environment

2021 ◽  
Author(s):  
Dennis Wolf ◽  
Andreas Engel ◽  
Tajas Ruschke ◽  
Andreas Koch ◽  
Christian Hochberger
Keyword(s):  
Computing System ◽  
Coarse Grained ◽  
Instruction Level Parallelism ◽  
Control Engineering ◽  
Processing Elements ◽  
Actual Application ◽  
Reconfigurable Arrays ◽  
Engineering Environment ◽  
On Chip ◽  
Level Parallelism

AbstractCoarse Grained Reconfigurable Arrays (CGRAs) or Architectures are a concept for hardware accelerators based on the idea of distributing workload over Processing Elements. These processors exploit instruction level parallelism, while being energy efficient due to their simplistic internal structure. However, the incorporation into a complete computing system raises severe challenges at the hardware and software level. This article evaluates a CGRA integrated into a control engineering environment targeting a Xilinx Zynq System on Chip (SoC) in detail. Besides the actual application execution performance, the practicability of the configuration toolchain is validated. Challenges of the real-world integration are discussed and practical insights are highlighted.

Hybrid Software Redundancy Approach for Building Reliable Communication in Multi-BUS Heterogeneous Systems

2016 ◽  
Vol 23 (04) ◽  
pp. 1650013
Author(s):  
Chafik Arar ◽  
Mohamed Salah Khireddine
Keyword(s):  
Fault Tolerant ◽  
Scheduling Algorithm ◽  
Heterogeneous Systems ◽  
Reliable Communication ◽  
Data Dependencies ◽  
Data Scheduling ◽  
Static Scheduling ◽  
Heterogeneous Architectures ◽  
Multiple Processors ◽  
Hardware Faults

The paper proposes a new reliable fault-tolerant scheduling algorithm for real-time embedded systems. The proposed algorithm is based on static scheduling that allows to include the dependencies and the execution cost of tasks and data dependencies in its scheduling decisions. Our scheduling algorithm is dedicated to multi-bus heterogeneous architectures with multiple processors linked by several shared buses. This scheduling algorithm is considering only one bus fault caused by hardware faults and compensated by software redundancy solutions. The proposed algorithm is based on both active and passive backup copies to minimize the scheduling length of data on buses. In the experiments, the proposed methods are evaluated in terms of data scheduling length for a set of DSP benchmarks. The experimental results show the effectiveness of our technique.

Product-Oriented Sensitivity Analysis for Multistation Compliant Assemblies

2006 ◽  
Vol 129 (8) ◽  
pp. 844-851 ◽  
Author(s):  
Jianpeng Yue ◽  
Jaime A. Camelio ◽  
Melida Chin ◽  
Wayne Cai
Keyword(s):  
Product Performance ◽  
Sensitivity Index ◽  
Final Assembly ◽  
Sensitivity Indices ◽  
Dimensional Variation ◽  
Product And Process ◽  
Pattern Sensitivity ◽  
Sheet Metal Assembly

Dimensional variation in assembled products directly affects product performance. To reduce dimensional variation, it is necessary that an assembly be robust. A robust assembly is less sensitive to input variation from the product and process components, such as incoming parts, subassemblies, fixtures, and welding guns. In order to effectively understand the sensitivity of an assembly to input variation, an appropriate set of metrics must be defined. In this paper, three product-oriented indices, including pattern sensitivity index, component sensitivity index, and station sensitivity index, are defined. These indices can be utilized to measure the variation influence of a pattern, an individual part, and/or component, and components at a particular station to the dimensional quality of a final assembly. Additionally, the relationships among these sensitivity indices are established. Based on these relationships, the ranges of the sensitivity indices are derived. Finally, a case study of a sheet metal assembly is presented and discussed to illustrate the applicability of these metrics.

scholarly journals CaLRS: A Critical-Aware Shared LLC Request Scheduling Algorithm on GPGPU

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Jianliang Ma ◽  
Jinglei Meng ◽  
Tianzhou Chen ◽  
Minghui Wu
Keyword(s):  
Scheduling Algorithm ◽  
Global Memory ◽  
Request Sequence ◽  
Thread Level Parallelism ◽  
Level Parallelism ◽  
Memory Request ◽  
Request Service

Ultra high thread-level parallelism in modern GPUs usually introduces numerous memory requests simultaneously. So there are always plenty of memory requests waiting at each bank of the shared LLC (L2 in this paper) and global memory. For global memory, various schedulers have already been developed to adjust the request sequence. But we find few work has ever focused on the service sequence on the shared LLC. We measured that a big number of GPU applications always queue at LLC bank for services, which provide opportunity to optimize the service order on LLC. Through adjusting the GPU memory request service order, we can improve the schedulability of SM. So we proposed a critical-aware shared LLC request scheduling algorithm (CaLRS) in this paper. The priority representative of memory request is critical for CaLRS. We use the number of memory requests that originate from the same warp but have not been serviced when they arrive at the shared LLC bank to represent the criticality of each warp. Experiments show that the proposed scheme can boost the SM schedulability effectively by promoting the scheduling priority of the memory requests with high criticality and improves the performance of GPU indirectly.

VLIW DSP-Based Low-Level Instruction Scheme of Givens QR Decomposition for Real-Time Processing

2017 ◽  
Vol 26 (09) ◽  
pp. 1750129 ◽  
Author(s):  
Mohamed Najoui ◽  
Mounir Bahtat ◽  
Anas Hatim ◽  
Said Belkouch ◽  
Noureddine Chabini
Keyword(s):  
High Performance ◽  
Qr Decomposition ◽  
Numerical Linear Algebra ◽  
Instruction Level Parallelism ◽  
Management Approach ◽  
Real Time Processing ◽  
Low Level ◽  
Processor Architectures ◽  
Efficient Data ◽  
Level Parallelism

QR decomposition (QRD) is one of the most widely used numerical linear algebra (NLA) kernels in several signal processing applications. Its implementation has a considerable and an important impact on the system performance. As processor architectures continue to gain ground in the high-performance computing world, QRD algorithms have to be redesigned in order to take advantage of the architectural features on these new processors. However, in some processor architectures like very large instruction word (VLIW), compiler efficiency is not enough to make an effective use of available computational resources. This paper presents an efficient and optimized approach to implement Givens QRD in a low-power platform based on VLIW architecture. To overcome the compiler efficiency limits to parallelize the most of Givens arithmetic operations, we propose a low-level instruction scheme that could maximize the parallelism rate and minimize clock cycles. The key contributions of this work are as follows: (i) New parallel and fast version design of Givens algorithm based on the VLIW features (i.e., instruction-level parallelism (ILP) and data-level parallelism (DLP)) including the cache memory properties. (ii) Efficient data management approach to avoid cache misses and memory bank conflicts. Two DSP platforms C6678 and AK2H12 were used as targets for implementation. The introduced parallel QR implementation method achieves, in average, more than 12[Formula: see text] and 6[Formula: see text] speedups over the standard algorithm version and the optimized QR routine implementations, respectively. Compared to the state of the art, the proposed scheme implementation is at least 3.65 and 2.5 times faster than the recent CPU and DSP implementations, respectively.

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