Meeting 22-07-2013

Meeting UoB 22 July 2013

Present: JB, SC, MG, AW, JP, SH, KE, OR

Hardware Energy Monitoring Report (Ashley)

• Slow progress due to needing to get the hardware working
  • Software installation / OS issues
• Moved to use a previous version (V2) energy-monitor boards, since they are more suitable
• Problems using V3 to measure external device energy consumptions
  • Capable but the level of additional soldering required, lack of voltage divider and need for external resistors makes it less easy to use for V2.
• Benchmarks working and verification of their correctness being worked on.
  • TODO: Now a priority to push on internal verification code to the tests.
    • e.g. compare outputs to pre-computed correct and return result.
• When running the benchmarks, we should apply techniques such as externs or volatiles that can store e.g. the final result.
  • Dijkstra program not working, others are.
• TODO: Get the benchmarks (BEEBS / BBS) work for last year out there with more oomph (webs, publicity, workshops...)

• James 13th 14th September will give a talk at OSHCAMP on energy monitoring board design.

MAGEEC Blog posts

• Weekly (ish) blogs, from rotating members of the team.
• TODO: 22/07/2013: Jeremy this week for an intro
• TODO: 29/07/2013: Ashley next week for intro to energy monitoring hardware.
  • Ensure the draft is saved.
  • Andrew will perform the final publishing
• TODO: 05/07/2013: Moon, intro to his work.

Compiler Framework

• No new update on implementation, due to attending GCC meeting etc.
• GCC meeting feedback is that HPC community (inc. LLL) very interested into learning more about low power.
• Looked at the research questions posted from UoB discussion on the wiki.
  • Profile-directed optimisation is very powerful, perhaps most powerful
  • Joern to be quizzed by James about the kinds of profile data that comes out, since it can impact the machine learning.
    • Can some examples of data be generated so that it can be represented for ML learning.

MILEPOST Approach

• Normalised feature vector 0<= 1 using the number of instructions as the divider.
• Ran 1000 random on/off flags, then kept the top 5% of previously trained data.
• Question on whether or not the flags are orthogonal.
• MSc student is addressing:
  • Taking James' flags of significance and isolating these and exhaustively
  • Data set available by end of week.
  • Performance as metric.
• TODO: Paper on James' previous work? Of 130 flags in GCC only 13 make a difference [in our scenarios]. We want to understand why.
• Consider systematic (FFD) vs exhaustive vs random selection and their effectiveness.
  • Part of the MSc work will address this.
• Moon investigating which of the MILEPOST vectors are and are not useful. Needs a large data set of 150+ applications to check them.
  • Work on WCET to extract additional programs to help with this.
  • Look (longer term) at HPC space to augment these.