26,098 papers · page 389 of 1,305
Tiling is a key technique for data locality optimization and is widely used in high-performance implementations of dense matrix-matrix multiplication for multicore/manycore CPUs and GPUs. However, the irregular and matrix-dependent data access pattern of sparse matrix multiplicat…
Hardware accelerators are a promising solution to optimize the Total Cost of Ownership (TCO) of cloud datacenters. This paper targets the costly Transport Layer Security (TLS) and investigates the TLS acceleration for the widely-deployed event-driven TLS servers or terminators. O…
Neural networks (NNs) have archived high accuracy in many fields of machine learning such as image recognition. Since computations of NNs are heavy tasks, GPUs have been widely used to accelerate them. However, the problem sizes of NNs that can be computed are limited by GPU memo…
Recursive divide-&-conquer algorithms are known for solving dynamic programming (DP) problems efficiently on shared-memory multicore machines. In this work, we extend them to run efficiently also on manycore GPUs and distributed-memory machines without changing their basic struct…
Communication overhead is a well-known performance bottleneck in distributed Stochastic Gradient Descent (SGD), which is a popular algorithm to perform optimization in large-scale machine learning tasks. In this work, we propose a practical and effective technique, named Adaptive…
Popper is an experimentation protocol for conducting scientific explorations and writing academic articles following a DevOps approach. The Popper CLI tool helps researchers automate the execution and validation of an experimentation pipeline. In this tutorial we give an introduc…
Modern Datalog engines are employed in industrial applications such as graph-databases, networks, and static program analysis. To cope with vast amount of data, Datalog engines must employ parallel execution strategies, for which specialized concurrent data structures are of para…
Accurate and computationally efficient image alignment is a vital step in scientific efforts to understand the structure of the brain through electron microscopy images of neurological tissue. Connectomics is an emerging area of neurobiology that uses cutting edge machine learnin…
In this paper, we investigate the effectiveness of multiprocessor architectures with ISA-different cores for executing HPC workloads. Our envisioned design point in the heterogeneous architecture space is one with multiple cache-coherency domains, with each domain hosting cores o…
Traditional concurrent programming involves manipulating shared mutable state. Alternatives to this programming style are communicating sequential processes (CSP) [1] and actor [2] models, which share data via explicit communication. Rendezvous channel is the common abstraction f…
Driven by ever increasing performance demands of compute-intensive applications, supercomputing systems comprise more and more nodes. This growth is a significant burden for fast group communication primitives and also makes those systems more susceptible to failures of individua…
Graph analytics frameworks, typically based on Vertex-centric or Edge-centric paradigms suffer from poor cache utilization, irregular memory accesses, heavy use of synchronization primitives or theoretical inefficiency, that deteriorate overall performance and scalability. In thi…
General matrix multiplication (GEMM) plays a paramount role in a broad range of domains such as deep learning, scientific computing, and image processing. The primary optimization method is to partition the matrix into many tiles and exploit the parallelism within and between til…
This paper proposes Gswitch, a pattern-based algorithmic auto-tuning system that dynamically switches between optimization variants with negligible overhead. Its novelty lies in a small set of algorithmic patterns that allow for the configurable assembly of variants of the algori…
This tutorial provides a hands-on introduction to quantum computing. It will feature the three pillars, architectures, programming, and algorithms/applications of quantum computing. Its focus is on the applicability of problems to quantum computing from a practical point, with on…
We present the multi-dimensional elasticity support in IBM Streams 4.3. Automatic operator fusion and dynamic threading were introduced in IBM Streams 4.2, which made it easier to map distributed stream processing to multicore systems through a low-cost operator scheduler and thr…
Energy-aware architectures provide applications with a mix of low and high frequency cores. Selecting the best core configurations for running programs is very challenging. Here, we leverage compilation, runtime monitoring and machine learning to map program phases to their best …
Recent studies have shown promising performance benefits of pipelined stencil applications. An important factor for the computing efficiency of such pipelines is the granularity of a task. We presents GOPipe, the first granularity-oblivious programming framework for efficient pip…
Linearizability is a key correctness property for concurrent data types. Linearizability requires that the behavior of concurrently invoked operations of the data type be equivalent to the behavior in an execution where each operation takes effect at an instantaneous point of tim…
The current wave of advances in Deep Learning (DL) has led to many exciting challenges and opportunities for Computer Science and Artificial Intelligence researchers alike. Modern DL frameworks like Caffe2, TensorFlow, Cognitive Toolkit (CNTK), PyTorch, and several others have em…