Exploiting state-of-the-art x86 architectures in scientific computing

In recent years, general purpose x86 architectures have undergone significant modifications towards high performance computing capabilities. Lately, technologies like wider vector units or Fused Multiply-Add (FMA) instruction, which were mainly known from GPU arcitectures, have been introduced. In this paper, we examine the performance of current x86 architectures, namely Intel Sandy Bridge and AMD Bulldozer, for four different parallel workloads with different properties. These properties comprise optimally cache-blocked algorithms as well as adaptive grid structures resulting in memory latency and bandwidth bound executions. The achieved performance on both architectures is very promising, and, if extrapolated towards upcoming server silicon, can be regarded as on par with current high-end GPU based accelerators.