Performance Analysis and Optimization of Sparse Matrix-Vector Multiplication on Modern Multi- and Many-Core Processors

This paper presents a low-overhead optimizer for the ubiquitous sparse matrix-vector multiplication (SpMV) kernel. Architectural diversity among different processors together with structural diversity among different sparse matrices lead to bottleneck diversity. This justifies an SpMV optimizer that is both matrix- and architecture-adaptive through runtime specialization. To this direction, we present an approach that first identifies the performance bottlenecks of SpMV for a given sparse matrix on the target platform either through profiling or by matrix property inspection, and then selects suitable optimizations to tackle those bottlenecks. Our optimization pool is based on the widely used Compressed Sparse Row (CSR) sparse matrix storage format and has low preprocessing overheads, making our overall approach practical even in cases where fast decision making and optimization setup is required. We evaluate our optimizer on three x86-based computing platforms and demonstrate that it is able to distinguish and appropriately optimize SpMV for the majority of matrices in a representative test suite, leading to significant speedups over the CSR and Inspector-Executor CSR SpMV kernels available in the latest release of the Intel MKL library.Comment: 10 pages, 7 figures, ICPP 201

Coexisting scheduling policies boosting I/O Virtual Machines

Abstract. Deploying multiple Virtual Machines (VMs) running various types of workloads on current many-core cloud computing infrastructures raises an important issue: The Virtual Machine Monitor (VMM) has to efficiently multiplex VM accesses to the hardware. We argue that altering the scheduling concept can optimize the system’s overall performance. Currently, the Xen VMM achieves near native performance multiplexing VMs with homogeneous workloads. Yet having a mixture of VMs with different types of workloads running concurrently, it leads to poor I/O performance. Taking into account the complexity of the design and implementation of a universal scheduler, let alone the probability of being fruitless, we focus on a system with multiple scheduling policies that coexist and service VMs according to their workload characteristics. Thus, VMs can benefit from various schedulers, either existing or new, that are optimal for each specific case. In this paper, we design a framework that provides three basic coexisting scheduling policies and implement it in the Xen paravirtualized environment. Evaluating our prototype we experience 2.3 times faster I/O service and link saturation, while the CPU-intensive VMs achieve more than 80 % of current performance.