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Conference

Irregular Applications: Architectures and Algorithms 

About: Irregular Applications: Architectures and Algorithms is an academic conference. The conference publishes majorly in the area(s): Speedup & Sparse matrix. Over the lifetime, 93 publication(s) have been published by the conference receiving 802 citation(s).

Papers published on a yearly basis

Papers
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Proceedings ArticleDOI
15 Nov 2015
TL;DR: The compressed sparse fiber (CSF) a data structure for sparse tensors along with a novel parallel algorithm for tensor-matrix multiplication is introduced and offers similar operation reductions as existing compressed methods while using only a single tensor structure.
Abstract: The Canonical Polyadic Decomposition (CPD) of tensors is a powerful tool for analyzing multi-way data and is used extensively to analyze very large and extremely sparse datasets. The bottleneck of computing the CPD is multiplying a sparse tensor by several dense matrices. Algorithms for tensor-matrix products fall into two classes. The first class saves floating point operations by storing a compressed tensor for each dimension of the data. These methods are fast but suffer high memory costs. The second class uses a single uncompressed tensor at the cost of additional floating point operations. In this work, we bridge the gap between the two approaches and introduce the compressed sparse fiber (CSF) a data structure for sparse tensors along with a novel parallel algorithm for tensor-matrix multiplication. CSF offers similar operation reductions as existing compressed methods while using only a single tensor structure. We validate our contributions with experiments comparing against state-of-the-art methods on a diverse set of datasets. Our work uses 58% less memory than the state-of-the-art while achieving 81% of the parallel performance on 16 threads.

100 citations

Proceedings ArticleDOI
16 Nov 2014
TL;DR: This paper shows the first scalable GPU implementation for triangle counting using a new list intersection algorithm called Intersect Path (named after the Merge Path algorithm), which has two levels of parallelism.
Abstract: Triangle counting in a graph is a building block for clustering coefficients which is a widely used social network analytic for finding key players in a network based on their local connectivity. In this paper we show the first scalable GPU implementation for triangle counting. Our approach uses a new list intersection algorithm called Intersect Path (named after the Merge Path algorithm). This algorithm has two levels of parallelism. The first level partitions the vertices to the streaming multiprocessors on the GPU. The second level is responsible for parallelizing the work across the GPU's streaming processors and utilizing different block sizes. For testing purposes, we used graphs taken from the DIMACS 10 Graph Challenge. Our experiments were conducted on NVIDIA's K40 GPU. Our GPU triangle counting implementation achieves speedups in the range of 9X -- 32X over a CPU sequential implementation.

60 citations

Proceedings ArticleDOI
13 Nov 2016
TL;DR: A new, highly-scalable PGAS memory-centric system architecture where migrating threads travel to the data they access, and a comparison of key parameters with a variety of today's systems, of differing architectures, indicates the potential advantages.
Abstract: There is growing evidence that current architectures do not well handle cache-unfriendly applications such as sparse math operations, data analytics, and graph algorithms. This is due, in part, to the irregular memory access patterns demonstrated by these applications, and in how remote memory accesses are handled. This paper introduces a new, highly-scalable PGAS memory-centric system architecture where migrating threads travel to the data they access. Scaling both memory capacities and the number of cores can be largely invisible to the programmer.The first implementation of this architecture, implemented with FPGAs, is discussed in detail. A comparison of key parameters with a variety of today's systems, of differing architectures, indicates the potential advantages. Early projections of performance against several well-documented kernels translate these advantages into comparative numbers. Future implementations of this architecture may expand the performance advantages by the application of current state of the art silicon technology.

48 citations

Proceedings ArticleDOI
13 Nov 2016
TL;DR: The optimized design and implementation of sparse tensor-times-dense matrix multiply (SpTTM) for CPU and GPU platforms is presented, which is a critical bottleneck in data analysis and mining applications based on tensor methods, such as the Tucker decomposition.
Abstract: This paper presents the optimized design and implementation of sparse tensor-times-dense matrix multiply (SpTTM) for CPU and GPU platforms. This primitive is a critical bottleneck in data analysis and mining applications based on tensor methods, such as the Tucker decomposition. We first design and implement sequential SpTTM to avoid explicit data transformations between a tensor and a matrix, which is the conventional approach. We further optimize SpTTM on multicore CPU and GPU systems by parallelizing, avoiding locks, and exploiting data locality. Our sequential SpTTM is up to 3.5× faster than the SpTTM from Tensor Toolbox and 1.5× over that from Cyclops Tensor Framework. Our parallel algorithms show 4.1× speedup on multicore Intel Core i7 and 18.8× speedup on NVIDIA K40c GPU over our sequential SpTTM respectively.

34 citations

Proceedings ArticleDOI
15 Nov 2015
TL;DR: A task-based formulation of Scalable Universal Matrix Multiplication Algorithm (SUMMA), a popular algorithm for matrix multiplication, is applied to the multiplication of hierarchy-free, rank-structured matrices that appear in the domain of quantum chemistry (QC).
Abstract: A task-based formulation of Scalable Universal Matrix Multiplication Algorithm (SUMMA), a popular algorithm for matrix multiplication (MM), is applied to the multiplication of hierarchy-free, rank-structured matrices that appear in the domain of quantum chemistry (QC). The novel features of our formulation are: (1) concurrent scheduling of multiple SUMMA iterations, and (2) fine-grained task-based composition. These features make it tolerant of the load imbalance due to the irregular matrix structure and eliminate all artifactual sources of global synchronization. Scalability of iterative computation of square-root inverse of block-rank-sparse QC matrices is demonstrated; for full-rank (dense) matrices the performance of our SUMMA formulation usually exceeds that of the state-of-the-art dense MM implementations (ScaLAPACK and Cyclops Tensor Framework).

33 citations

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Performance
Metrics
No. of papers from the Conference in previous years
YearPapers
20208
201911
20189
201711
201614
201513