Integrate x-mem for numa aware memory benchmarking

[jellyfishcake]

https://github.com/microsoft/X-Mem

This task adds X-Mem as a new benchmark for PKB. It can be divided into a few different components:

1. Add the benchmarking framework

• create xmem_benchmark.py file in linux_benchmarks
• create xmem_benchmark.py in windows benchmarks
• populate BENCHMARK_NAME and BENCHMARK_CONFIG constants so that the benchmark can be found by PKB
• create GetConfig, Prepare, Run and Cleanup functions handlers with pass/return [] as function content.
  At this point you can run your new benchmark in PKB (though it will not do anything yet).

2. Install xmem

• Download the xmem installer (use the OS that you will prefer to add to PKB) and try installing on your machine.
• Create a new package called xmem.py in linux_packages/windows_packages.
• create a new function, Install, that takes a vm as an input and installs xmem on that vm. You should be able to wrap shell commands as vm.RemoteCommand
• Install for windows and ubuntu2404 are top priority, other linux OSes are nice to have.
• add unit tests to make sure the remote commands are issued using mock to mock the vm

3. Run xmem

• Download and run xmem locally, you don't need to use PKB to run xmem. This part is about parsing the output from xmem into some sensible format.
• Store the xmem output as a file in the data directory under data/xmem. This file will be your raw data for parsing and parsing unit tests.
  Add a function in xmem.py that you added to linux_packages/windows_packages with a sensible name, e.g. ParseResults
  Parse results should take a str as input and produce a list of PKB Samples as output. You goal is to parse the output into useful samples, where each sample as a metric name, metric value, metric unit, metric metadata. Each row of Geekbench's output should be a separate metric.
  Test the parser function