scholarly journals A Cross-Platform Infrastructure for Scalable Runtime Application Performance Analysis

2005 ◽  
Author(s):  
Jack Dongarra ◽  
Shirley Moore ◽  
Jeffrey Hollingsworth Bart Miller ◽  
Tracy Rafferty
Keyword(s):  
Performance Analysis ◽  
Application Performance ◽  
Cross Platform

SPIFFY SHIRTS E-COMMERCE STORE (CROSS PLATFORM APPLICATION)

2021 ◽  
Vol 1 (10) ◽  
pp. 295-301
Author(s):  
Vipul Kumar
Keyword(s):  
Performance Analysis ◽  
Administrative Functions ◽  
Different Types ◽  
Cross Platform ◽  
Payment Gateway

In today's generation, most people are using technology for leading their lives and fulfilling their daily needs. In this generation most of us using E-commerce for shopping for clothes, groceries, and electronics (Chanana and Goele, 2012).We have developed one E-commerce cross platform application by using MERN stack and PWA technology as it contains MongoDB, Express.JS framework, React.JS library, Node.JS platform. PWA technology is applied to enable users to access a native-like mobile version of their favorite website with a single tap.This application is fully functional with different views for user and admin and it also has integrated with payment gateway for checkout. By using this website we can buy different types of t-shirts and we can choose different styles of t-shirts based upon customer interests. In this project, we can add different products and can delete them also. We have developed administrative functions for the application such as create a product, create categories, Admin dashboard, Manage products, Manage categories. For customers, they can quickly add their items to the cart. Based on the items in the cart, the bill gets generated and the customer can pay by using stripe. (Mai,2020) Keywords: JavaScript, Software Stack, Framework, Library, Performance Analysis, React.js, MongoDB, Node.js, Express.js.

A Scalable Approach to MPI Application Performance Analysis

2005 ◽  
pp. 309-316 ◽  
Author(s):  
Shirley Moore ◽  
Felix Wolf ◽  
Jack Dongarra ◽  
Sameer Shende ◽  
Allen Malony ◽  
...  
Keyword(s):  
Performance Analysis ◽  
Application Performance

A PERFORMANCE ANALYSIS OF THREE GENERATIONS OF BLUE GENE

2013 ◽  
Vol 23 (04) ◽  
pp. 1340007
Author(s):  
DARREN J. KERBYSON ◽  
KEVIN J. BARKER ◽  
DIEGO S. GALLO ◽  
DONG CHEN ◽  
JOSE R. BRUNHEROTO ◽  
...  
Keyword(s):  
Performance Analysis ◽  
System Integration ◽  
Power Efficiency ◽  
Performance Model ◽  
Peak Performance ◽  
Comparative Performance ◽  
Application Performance ◽  
Successive Generations ◽  
Blue Gene ◽  
System Configurations

IBMs Blue Gene supercomputer architecture has evolved through three successive generations each providing increased levels of power-efficiency and system densities. From the original Blue Gene/L to P to Q, a higher level of integration has enabled higher single-core performance, larger concurrency per compute node, and a higher level of system integration. Although these changes have brought with them a higher overall system peak-performance, no study has examined in detail the evolution of performance across system generations. In this work we make two significant contributions that of providing a comparative performance analysis across Blue Gene generations using a consistent set of tests, and also in providing a validated performance model of the NEK-Bone proxy application from the DOE CESAR Exascale Co-Design Center. The combination of empirical analysis and the predictive capabilities of the NEK-Bone performance model enable us to not only directly compare measured performance but also allow for a comparison of system configurations that cannot currently be measured. We provide insights into how the changing architectural performance characteristics of Blue Gene have impacted on the application performance, as well as providing insight into what future systems may be able to achieve.

Only Relative Speed Matters

2021 ◽  
Vol 48 (3) ◽  
pp. 113-119
Author(s):  
Behnam Pourghassemi ◽  
Ardalan Amiri Sani ◽  
Aparna Chandramowlishwaran
Keyword(s):  
Performance Analysis ◽  
Unique Property ◽  
Necessary Condition ◽  
Network Storage ◽  
Relative Speed ◽  
Seminal Paper ◽  
Major Bottleneck ◽  
Cross Platform ◽  
Potential Impact ◽  
Secondary Device

Causal profiling is a novel and powerful profiling technique that quantifies the potential impact of optimizing a code segment on the program runtime. A key application of causal profiling is to analyze what-if scenarios which typically require a large number of experiments. Besides, the execution of a program highly depends on the underlying machine resources, e.g., CPU, network, storage, so profiling results on one device does not translate directly to another. This is a major bottleneck in our ability to perform scalable performance analysis and greatly limits cross-platform software development. In this paper, we address the above challenges by leveraging a unique property of causal profiling: only relative performance of different resources affects the result of causal profiling, not their absolute performance. We first analytically model and prove causal profiling, the missing piece in the seminal paper. Then, we assert the necessary condition to achieve virtual causal profiling on a secondary device. Building upon the theory, we design VCoz, a virtual causal profiler that enables profiling applications on target devices using measurements on the host device. We implement a prototype of VCoz by tuning multiple hardware components to preserve the relative execution speeds of code segments. Our experiments on benchmarks that stress different system resources demonstrate that VCoz can generate causal profiling reports of Nexus 6P (an ARM-based device) on a host MacBook (x86 architecture) with less than 16% variance.

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