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RESEARCH 》 Introducing TrueBench - a high resolution CPU benchmarking system

TrueBench
TrueBench is an unique open-source benchmarking system in which the core system performance and efficiency parameters are measured at extreme high resolution in the order of several million/billion µ-seconds for a given specific task. TrueBench is a part of The TOFFEE Project research. With TrueBench Raspberry Pi 3, Raspberry Pi 2B, Raspberry Pi 2 and other embedded SoC devices are benchmarked and you can do a comparative analysis with standard mainstream x86 devices.

TrueBench can be used to measure specific system performance of user-space application performance in a systems software point-of-view. With this technique you can isolate multi-core CPU performance, multi-threading CPU performance and derive a precise conclusion about the CPU's true potential.

Advantages:

  • evaluate CPU's current generation versus previous generation architecture
  • multi-platform(x86 32-bit, x86_64, ARM, MIPS)
  • light-weight Linux user-space app which restricts the choice of OS for precise measurements
  • high resolution benchmarking technique
  • precise combination of instructions for over a trillion iterations per test
  • open-source - can be compiled for any Linux based hardware

Applications(use-cases):

  • building low-latency high performance networking devices
  • embedded/SoC cpu (platform) evaluation
  • server and datacenter hardware evalation
  • new product design/architecture evalation
  • scientific applications (such as HPC, Super-Computers, etc)

For more details: visit TrueBench



Suggested Topics:


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💎 TOFFEE-MOCHA new bootable ISO: Download
💎 TOFFEE Data-Center Big picture and Overview: Download PDF


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My sample Wireshark packet capture files for research ↗
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Featured Educational Video:
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VPN Network Optimization via TOFFEE WAN Optimization ↗
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VPN Networks may degrade network performance due to various packet processing overheads such as encryption and by adding extra network protocol header(s) (such as IPv4/IPv6, IPSec, etc). This may inflate near MTU sized packets and causes excessive packet fragmentation. Here are the few examples of packet processing involved in a VPN (or a VPN like) Tunnel. With TOFFEE you can optimize these packets even before they get processed on to a VPN device. TOFFEE optimizes packet contents (application payload and transport headers) so that these TOFFEE optimized packets when they get processed by VPN devices (or VPN software stack) they may never need further packet fragmentation. Here is a deployment scenario of TOFFEE with VPN devices.

TOFFEE-DataCenter - First Live Demo and software development - Update: 26-Aug-2016 ↗
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Today I have done a test setup so that I can able to connect my Android Samsung Tab via TOFFEE DataCenter. Below is my complete test topology of my setup. For demo (and research/development) context I configured TOFFEE DataCenter in engineering debug mode. So I do not need two devices for this purpose.

A study on Deep Space Networks (DSN) ↗
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Optimization of network data (WAN Optimization) at various levels ↗
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TOFFEE deployment topology guide ↗
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Assume you have two sites (such as Site-A and Site-B) connected via slow/critical WAN link as shown below. You can optimize this link by saving the bandwidth as well possibly improve the speed. However, the WAN speed can be optimized only if the WAN link speeds are below that of the processing latency of your TOFFEE installed hardware. Assume your WAN link is 12Mbps, and assume the maximum WAN optimization speed/capacity of Raspberry Pi is 20Mbps, then your link will get speed optimization too. And in another case, assume your WAN link is 50Mbps, then using the Raspberry Pi as WAN Optimization device will actually increase the latency (i.e slows the WAN link). But in all the cases the bandwidth savings should be the same irrespective of the WAN link speed. In other words, if you want to cut down the WAN link costs via this WAN Optimization set up, you can always get it since it reduces the overall bandwidth in almost all the cases (including encrypted and pre-compressed data).



Research :: Optimization of network data (WAN Optimization) at various levels:
Network File level network data WAN Optimization


Learn Linux Systems Software and Kernel Programming:
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