Wilkinson defined a sparse matrix as one with enough zeros that it pays to take advantage of
them. 1 This informal yet practical definition captures the essence of the goal of direct …

Processing a one trillion-edge graph has recently been demonstrated by distributed graph
engines running on clusters of tens to hundreds of nodes. In this paper, we employ a single …

The massive explosion in social networks has led to a significant growth in graph analytics
and specifically in dynamic, time-varying graphs. Most prior work processes dynamic graphs …

Graph algorithms are challenging to parallelize on manycore architectures due to complex
data dependencies and irregular memory access. We consider the well studied problem of …

In recent years, a large number of graph processing frameworks have been introduced, with
their goal to simplify analysis of real-world graphs on commodity hardware. Additionally, the …

Quantifying simulation uncertainties is a critical component of rigorous predictive simulation.
A key component of this is forward propagation of uncertainties in simulation input data to …

Transient simulation in circuit simulation tools, such as SPICE and Xyce, depend on
scalable and robust sparse LU factorizations for efficient numerical simulation of circuits and …

We present a sparse matrix vector multiplication (SpMV) kernel that uses a novel sparse
matrix storage format and delivers superior performance for unstructured matrices on Intel …

Online arithmetic, where computations are performed from the most significant digit first, has
shown benefits in improving throughput and latency within high-performance computing …

Detecting strongly connected components (SCC) is a practical graph analytics algorithm
widely used in many application domains. To accelerate SCC detection, parallel algorithms …