EdCrunch 2018 - Skyeng - EdTech product scaling: How to influence key growth ...Michael Karpov
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Skyeng company case:
"EdTech product scaling: How to influence key growth indicators and achieve rapid progress. Product VS Marketing look"
Global conference for technology in education #EdCrunch
https://2018.edcrunch.ru/en/
Movement to business goals: Data, Team, Users (4C Conference)Michael Karpov
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In this talk Mikhail Karpov discuss the methods used to move to business goals faster on example of VK.com processes, including teams flexible structure and feedback loop from service audience
Hpc Visualization with X3D (Michail Karpov)Michael Karpov
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The document describes two modes of data analysis for workload rendering on server clusters:
1. General system analysis uses a molecular metaphor to visualize task load across cluster nodes over time, allowing users to identify bottlenecks.
2. Detailed task analysis uses a greenhouse metaphor to test individual tasks under varying hardware/software parameters and identify bottlenecks like CPU, memory, or disk limitations.
Both modes aim to optimize workload distribution and resource usage across clusters.
The document describes a software system being developed to visually monitor the workload of cores in a high-performance manycore computer architecture. The system receives data about the state of cores in a computing system, analyzes the data, and displays it visually with remote web access. Compared to other software for visually monitoring multiprocessor systems, this system provides a visual display of processed data on the state of cores based on analysis of inter-core messages and characteristics of individual cores. The system is being developed using Microsoft Visual Studio 2008 on a 16-core Windows cluster at Polytechnic University and will aid in analyzing and monitoring complex systems and their components during different workload modes.
The document discusses the development of a system for visual monitoring of workloads on high-performance multi-core computer clusters. The system provides visual analysis and performance monitoring of clusters and their components. It was developed using Microsoft tools on a 16-node Windows HPC Server 2008 cluster. The system displays program characteristics, core memory usage, and process status to help optimize parallel programs.