D-type Conformal Matter and SU/USp Quivers

We discuss the four dimensional models obtained by compactifying a single M5 brane probing $D_{N}$ singularity (minimal D-type $(1,0)$ conformal matter in six dimensions) on a torus with flux for abelian subgroups of the $SO(4N)$ flavor symmetry. We derive the resulting quiver field theories in four dimensions by first compactifying on a circle and relating the flux to duality domain walls in five dimensions. This leads to novel ${\cal N}=1$ dualities in 4 dimensions which arise from distinct five dimensional realizations of the circle compactifications of the D-type conformal matter.Comment: 38 pages, 13 figure

Star shaped quivers in four dimensions

We discuss a 4d Lagrangian descriptions, across dimensions IR dual, of compactifications of the 6d $(\text{D},\text{D})$ minimal conformal matter theory on a sphere with arbitrary number of punctures and a particular value of flux as a gauge theory with a simple gauge group. The Lagrangian has the form of a ``star shaped quiver'' with the rank of the central node depending on the 6d theory and the number and type of punctures. Using this Lagrangian one can construct across dimensions duals for arbitrary compactifications (any, genus, any number and type of $\text{USp}$ punctures, and any flux) of the $(\text{D},\text{D})$ minimal conformal matter gauging only symmetries which are manifest in the UV.Comment: 7 pages, 4 figure

FairGV: Fair and Fast GPU Virtualization

Increasingly high-performance computing (HPC) application developers are opting to use cloud resources due to higher availability. Virtualized GPUs would be an obvious and attractive option for HPC application developers using cloud hosting services. Unfortunately, existing GPU virtualization software is not ready to address fairness, utilization, and performance limitations associated with consolidating mixed HPC workloads. This paper presents FairGV, a radically redesigned GPU virtualization system that achieves system-wide weighted fair sharing and strong performance isolation in mixed workloads that use GPUs with variable degrees of intensity. To achieve its objectives, FairGV introduces a trap-less GPU processing architecture, a new fair queuing method integrated with work-conserving and GPU-centric co-scheduling polices, and a collaborative scheduling method for non-preemptive GPUs. Our prototype implementation achieves near ideal fairness (? 0.97 Min-Max Ratio) with little performance degradation (? 1.02 aggregated overhead) in a range of mixed HPC workloads that leverage GPUs