the u.s. Department of energy’s Jaguar supercomputer took home a trio of gold medals—for speed, sustainable memory bandwidth, and for fft execution—at the recent hPc challenge competition.

IMAGe CouRTesy oF The nATIonAl Cen TeR FoR CoMPu TATIonAl sCIenCes, oAK RIDGe nATIonAl lAboRAToRy

familiar HPC benchmarks in the new languages could reduce source-code volume substantially—tenfold reductions were not surprising—but rewriting these benchmarks even in Fortran achieved similar source-code reductions and corresponding improvements in expressivity.

New programming languages still have much to offer, for example, in the areas of expressing concurrency and especially data distribution. It’s just that the bloat we see in current HPC source code stems not so much from inadequacies in current languages as from other factors.

What We Did

We rewrote a number of HPC bench-
marks and applications using mod-
ern Fortran in a way that took into ac-
coount the human costs of software
development: programmability and
associated characteristics such as
readability, verifiability, and main-
tainability. These are important con-
siderations; although copy-and-paste
is a fast way of writing lines of code,
it degrades readability and increases
maintenance costs.

In this article, we focus on the output of the rewriting activity, examining the rewritten HPC programs and causes of source-code bloat. The particular HPC test codes used here are the NPBs (NAS Parallel Benchmarks) CG, MG, and BT; the plasma fusion application GTC; and the 3D hydrodynamics code sPPM.

A key metric was the number of source lines of code (SLOC). This is admittedly a crude and often deceptive metric, but it served as a convenient starting point for quantifying readability and expressivity of source code.

Since the generated computer programs were in Fortran, they could be compiled and run. Thus, we were able to study their performance relative to the original code, test automatic par-