We describe, implement and benchmark EigenCFA, an algorithm for accelerating higher-order control-flow analysis (specifically, 0CFA) with a GPU. Ultimately, our program transformations, reductions and optimizations achieve a factor of 72 speedup over an optimized CPU implementation.

In this paper we present a novel cluster paradigm and silicon operating system. Our approach in developing the competent cluster design revolves around an execution model to aid the execution of multiple independent applications simultaneously on the cluster, leading to cost sharing across applications. The execution model should envisage simultaneous execution of multiple applications (running traces of multiple independent applications in the same node at an instant, without time sharing) and on all the partitionsf nodes) of a single cluster, without sacrificing the performance of individual application, unlike in the current cluster models. Performance scalability is achieved as we increase the number of nodes, the problem size of the individual independent applications, due to non-dependency across applications and hence increase in the number of non-dependent operations (as the problem sizes of the applications get increased) and this leads to better utilization of the unused resources within the node. This execution model is very much dependent on the node architecture for performance scalability. This would be a major initiative towards achieving performance cost-effective supercomputing.