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Sparse Matrix-Vector multiplication (SpMV) is a fundamental kernel, used by a large class of numerical algorithms. Emerging big-data and machine learning applications are propelling a renewed interest in SpMV algorithms that can tackle massive amount of unstructured data-rapidly approaching the TeraByte range-with predictable, high performance. In this paper we describe a new methodology to design SpMV algorithms for shared memory multiprocessors (SMPs) that organizes the original SpMV algorithm into two distinct phases. In the first phase we build a scaled matrix, that is reduced in the second phase, providing numerous opportunities to exploit memory locality. Using this methodology, we have designed two algorithms. Our experiments on irregular big-data matrices (an order of magnitude larger than the current state of the art) show a quasi-optimal scaling on a large-scale POWER8 SMP system, with an average performance speedup of 3.8×, when compared to an equally optimized version of the CSR algorithm. In terms of absolute performance, with our implementation, the POWER8 SMP system is comparable to a 256-node cluster. In terms of size, it can process matrices with up to 68 billion edges, an order of magnitude larger than state-of-the-art clusters. CCS Concepts•Computing methodologies → Linear algebra algorithms; Shared memory algorithms; Vector / streaming algorithms; •Mathematics of computing → Graph algorithms; •Theory of computation → Graph algorithms analysis; Data structures design and anal- * Fabrizio Petrini has since changed his affiliation. His current contact is fabrizio.petrini@intel.com ACM acknowledges that this contribution was authored or co-authored by an employee, or contractor of the national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only. Permission to make digital or hard copies for personal or classroom use is granted. Copies must bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. To copy otherwise, distribute, republish, or post, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org.

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We show that a variational calculation of the ground-state energy of SUC/V) Hamiltonian lattice gauge theories concurrently gives an estimate of the 0 ++ glueball mass. Monte Carlo calculations in three spatial dimensions for N mm 3, 4, 5, and 6 indicate that the resulting glueball mass begins to scale for7V>5.