With the graphs below you can compare calculation time and cost for a fixed amount of calculations in Floating Point Operations (FLOPs***). Use buttons above the graphs to set calculations amount and number of virtual or bare metal machines called “nodes” used for calculations.
Important notice: we assume that a task can be run on multiple computers WITHOUT any slowdown. This means that on N machines the task will finish N times faster. This could be true, for instance, in case of hyperparameters search when you have multiple independant tasks.
Graphs are not scaled when parameters change to make points movements clearly visible. To scale graphs manually use “Autoscale” and “Reset axes” buttons that appear in the top right corner of the graphs when you bring the mouse cursor over it (on tablet devices tap the graph).
There are plenty of cloud GPU offers from many providers. This post is here to help you to compare offers in terms of cost*, GPU and CPU performance**, memory etc. It has some interactive graphs for comparing offers and a table with details for each offer.
The “filter” charts below provide statistical information about offers distribution by some parameters, such as how many offers each provider has. These charts can also be used for filtering offers. Click on a value in any chart to filter out offers with different values. You can select multiple values on one or multiple charts. All graphs and the table below will show data only for the selected offers.
Please note, that only offers with GPU are mentioned on this page. Some providers, like Google and Amazon, have too many offers to show them all here, so I picked up only some representative ones.
This chart depicts a structure of Docker-related tools in terms of their functionality. Docker ecosystem is ever changing, so is this chart. I plan to update it more or less regularly. Any suggestions on how to improve it are welcome.
Last update 2016/10/14
Tools for registering and searching information about services provided by applications running in containers (including multi-host applications).
Tools with main purpose of managing multi-host multi-container applications. Usually help managing multiple containers and network connections between them.
Tools that help :
a. making containers easier to use,
b. giving containers new features,
c. building a service powered by containers.
Tools for monitoring resources used by containers, containers heath- check, monitoring in-container environment.
Light-weight OS for running containers.
Tools for organising inter-container and host-container communications.
Data and File Systems
Tools for managing data in containers and tools that include or control Docker file system plugins.
Note: Tools’ features presented on the chart are based on what is advertised on the tool web site or on information provided by the tool developers.
Tested on Dell Poweredge D320 server with Xeon 1.8GHz, 4GB (1333MHz) RDIMM, 7200RPM SATA HDD
OS Ubuntu server 12.04.4 LTS
Docker version 0.11.
Network performance was tested with iperf with the following client command: iperf -c $ServerIP -P 1 -i 1 -p 5001 -f g -t 5
Performance in Gbits/s, average for one container.
The following 3 setups was tested:
One server container and multiple client containers with 1 iperf process in each container.
Multiple servers and multiple clients. 1 iperf process in each container.
One server container with one iperf server, multiple containers with multiple iperf clients in each container.
One server and multiple clients
ICC performance between one container with one iperf server and multiple containers with one iperf client each. Average performance per container in Gbits/s.
Multiple servers and multiple clients
ICC between multiple containers each with one iperf server inside, and multiple containers with one iperf client each. Client number i connects to the server number i (mod n), where n is the number of servers.
Below are the average performance results in Gbits/s.
One server and multiple containers with multiple iperf clients inside
Average performance per container in Gbits/sec.
Number of containers x number of iperf clients in one container
1 x 1
1 x 2
1 x 4
1 x 16
2 x 1
4 x 1
16 x 1
2 x 2
4 x 4
16 x 16
Running iperf clients in different containers gives better performance compared with the same number of clients running in one container (compare 1×4 and 4×1).