I just received my annual TCP-1 letter from LLNS and a summary of the LLNS Pension Plan. Looked in pretty good shape in 2013. About 35% overfunded (funding target attainment percentage = 134.92%). This was a decrease from 2012 where it was 51% overfunded (funding target attainment percentage = 151.59%). They did note that the 2012 change in the law on how liabilities are calculated using interest rates improved the plan's position. Without the change the funding target attainment percentages would have been 118% (2012) and 105% (2013). 2013 assets = $2,057,866,902 2013 liabilities = $1,525,162,784 vs 2012 assets = $1,844,924,947 2012 liabilities = $1,217,043,150 It was also noted that a slightly different calculation method ("fair market value") designed to show a clearer picture of the plan' status as December 31, 2013 had; Assets = $2,403,098,433 Liabilities = $2,068,984,256 Funding ratio = 116.15% Its a closed plan with 3,781 participants. Of that number, 3,151 wer...
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Of course, many scientific codes involve computations such as modeling events in several dimensions or in the quantum domain, and suffer from unfavorable scaling in any case, so the algorithm in use may play a greater role in changing the scaling power law or exponent, it may not be that important to lower the prefactor.
Better operating systems, languages, and compilers and scheduling algorithms can help performance though, and this could be part of the answer.
In real code environments, code maintenance and refactoring can also be an issue, code can be bloated or intentionally obfuscated by developers, and of course in high performance computing there can be perverse incentives to utilize more resources rather than less, and so on. Code features also become bloated and obfuscated, as in Microsoft office and Windows, as a way of generating a technological moat, driving ever-greater computing needs for basic tasks.
Maybe AI coding tools will cut this Gordian knot and lead to more efficient and maintainable code, it could also erode some of the power of entrenched platforms and operating systems at the big tech companies, leading to a new wave of innovative and performant software.