Beyond the issues of languages and mindset, many problem domains parallelize poorly. Too many important phenomena fundamentally involve feedback loops evolving over time, and when that happens you can't just compute f(t) and f(t + 1) on different cores. At that point, throwing more cores at the problem might let you make the model bigger, but will very quickly hit a wall in terms of making it faster.