On Confident Task-Accurate Performance Estimation

2013 ◽  
pp. 25-37
Author(s):  
Yang Xu ◽  
Bo Wang ◽  
Rafael Rosales ◽  
Ralph Hasholzner ◽  
Jürgen Teich
Keyword(s):  
Performance Estimation ◽  
Accurate Performance

Quantum Optimization of Evolutionary Algorithm Using Droplet-Based Advanced Digital Microfluidic Biochip and Clinical Prognosis Configuration

2019 ◽  
Vol 9 (6) ◽  
pp. 1329-1336
Author(s):  
G. Brindha ◽  
G. Rohini
Keyword(s):  
Evolutionary Algorithm ◽  
Research Work ◽  
Performance Estimation ◽  
System Configuration ◽  
Clinical Prognosis ◽  
Accurate Performance ◽  
Micro Array ◽  
Static Power ◽  
Digital Microfluidic ◽  
Quantum Optimization

In this research work M × N, DNA micro array is utilized with the help of Quantum optimization of evolutionary algorithm for accurate performance estimation in the system configuration provides high throughput using clinical prognosis application based on image processing. In an existing system the droplet-manipulation method based on a "cross-referencing" method that is used for "row" and "columns" to access electrodes. In our research work proposed Advanced Digital Micro Fluidic Biochip (ADMFB) process is a synthesis configuration of linear way dynamic routing segment used faster execution span related to the previous bio chip module. These techniques are minimizing the consumption of power and area. Experimental outputs shows the improvement in the static power, dynamic power and delay while comparing the previous research work and proposed research work.

SystemC Platform Modeling for Behavioral Simulation and Performance Estimation of Embedded Systems

2010 ◽  
pp. 219-243 ◽  
Author(s):  
Hector Posadas ◽  
Juan Castillo ◽  
David Quijano ◽  
Victor Fernandez ◽  
Eugenio Villar ◽  
...  
Keyword(s):  
Embedded Systems ◽  
Design Space Exploration ◽  
Performance Metrics ◽  
System Modeling ◽  
Performance Estimation ◽  
Behavioral Simulation ◽  
Close Interaction ◽  
Accurate Performance ◽  
And Performance ◽  
On Chip

Currently, embedded systems make use of large, multiprocessing systems on chip integrating complex application software running on the different processors in close interaction with the application-specific hardware. These systems demand new modeling, simulation, and performance estimation tools and methodologies for system architecture evaluation and design exploration. Recently approved as IEEE 1666 standard, SystemC has proven to be a powerful language for system modeling and simulation. In this chapter, SCoPE, a SystemC framework for platform modeling, SW source-code behavioral simulation and performance estimation of embedded systems is presented. Using SCoPE, the application SW running on the different processors of the platform can be simulated efficiently in close interaction with the rest of the platform components. In this way, fast and sufficiently accurate performance metrics are obtained for design-space exploration.

Analytical Performance Estimation for Large-Scale Reconfigurable Dataflow Platforms

2021 ◽  
Vol 14 (3) ◽  
pp. 1-21
Author(s):  
Ryota Yasudo ◽  
José G. F. Coutinho ◽  
Ana-Lucia Varbanescu ◽  
Wayne Luk ◽  
Hideharu Amano ◽  
...  
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Large Scale ◽  
Heterogeneous Systems ◽  
Performance Estimation ◽  
Performance Impact ◽  
Accurate Performance ◽  
Computing Platforms ◽  
Reduced Power Consumption ◽  
Performance Computing

Next-generation high-performance computing platforms will handle extreme data- and compute-intensive problems that are intractable with today’s technology. A promising path in achieving the next leap in high-performance computing is to embrace heterogeneity and specialised computing in the form of reconfigurable accelerators such as FPGAs, which have been shown to speed up compute-intensive tasks with reduced power consumption. However, assessing the feasibility of large-scale heterogeneous systems requires fast and accurate performance prediction. This article proposes Performance Estimation for Reconfigurable Kernels and Systems (PERKS), a novel performance estimation framework for reconfigurable dataflow platforms. PERKS makes use of an analytical model with machine and application parameters for predicting the performance of multi-accelerator systems and detecting their bottlenecks. Model calibration is automatic, making the model flexible and usable for different machine configurations and applications, including hypothetical ones. Our experimental results show that PERKS can predict the performance of current workloads on reconfigurable dataflow platforms with an accuracy above 91%. The results also illustrate how the modelling scales to large workloads, and how performance impact of architectural features can be estimated in seconds.

On robust task-accurate performance estimation

2013 ◽  
Author(s):  
Yang Xu ◽  
Bo Wang ◽  
Ralph Hasholzner ◽  
Rafael Rosales ◽  
Jürgen Teich
Keyword(s):  
Performance Estimation ◽  
Accurate Performance
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