Improving instruction-level parallelism by loop unrolling and dynamic memory disambiguation

1995 ◽  
Author(s):  
J.W. Davidson ◽  
S. Jinturkar
Keyword(s):  
Instruction Level Parallelism ◽  
Dynamic Memory ◽  
Loop Unrolling ◽  
Memory Disambiguation ◽  
Improving Instruction ◽  
Level Parallelism

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.

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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