面向图DSL的多种加速器代码生成

Performance and Portability of Accelerated Lattice Boltzmann Applications with OpenACC

An increasingly large number of HPC systems rely on heterogeneous architectures combining traditional multi-core CPUs with power efficient accelerators. Designing efficient applications for these systems has been troublesome in the past as accelerators could usually be programmed using specific programming languages threatening maintainability, portability and correctness. Several new programming environments try to tackle this problem. Among them, OpenACC offers a high-level approach based on compiler directive clauses to mark regions of existing C, C++ or Fortran codes to run on accelerators…

GenVectorX：一个面向洛伦兹向量运算的性能可移植SYCL库

GenVectorX: A performance-portable SYCL library for Lorentz Vectors operations

本文提出GenVectorX，一个基于SYCL的C++库，用于表示和操作粒子事件中的洛伦兹向量，在NVIDIA GPU上实现了与CUDA相当的性能和可扩展性，为高能物理计算提供了性能可移植的解决方案。

An Efficient Graph Accelerator with Parallel Data Conflict Management

Graph-specific computing with the support of dedicated accelerator has greatly boosted the graph processing in both efficiency and energy. Nevertheless, their data conflict management is still sequential in essential when some vertex needs a large number of conflicting updates at the same time, leading to prohibitive performance degradation. This is particularly true for processing natural graphs. In this paper, we have the insight that the atomic operations for the vertex updating of many graph algorithms (e.g., BFS, PageRank and WCC) are typically incremental and simplex. This hence allows u…

Computing Graph Neural Networks: A Survey from Algorithms to Accelerators

Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent years owing to their capability to model and learn from graph-structured data. Such an ability has strong implications in a wide variety of fields whose data is inherently relational, for which conventional neural networks do not perform well. Indeed, as recent reviews can attest, research in the area of GNNs has grown rapidly and has lead to the development of a variety of GNN algorithm variants as well as to the exploration of groundbreaking applications in chemistry, neurology, electronics, or communication…

Groovy Parallel Patterns: A Process oriented Parallelization Library

A novel parallel patterns library, Groovy Parallel Patterns, is presented which, from the outset, has been designed to exploit more general process parallelism than the usual data and task parallel architectures. The library executes on a standard Java Virtual Machine. The library provides a collection of processes that can be plugged together to form a variety of parallel architectures and is intrinsically its own DSL. A network of processes is guaranteed to be deadlock and livelock free and terminate correctly and this is proved by the use of formal methods. Error capture and a basic logging…

PAL：一种面向GPU集群中ML工作负载调度的变异性感知策略

PAL: A Variability-Aware Policy for Scheduling ML Workloads in GPU Clusters

针对GPU集群中ML工作负载的性能变异性问题，提出变异性感知调度器PAL，通过刻画应用变异性并平衡局部性，显著提升作业完成时间、利用率和完工时间。

Slice-and-Forge: Making Better Use of Caches for Graph Convolutional Network Accelerators

Graph convolutional networks (GCNs) are becoming increasingly popular as they can process a wide variety of data formats that prior deep neural networks cannot easily support. One key challenge in designing hardware accelerators for GCNs is the vast size and randomness in their data access patterns which greatly reduces the effectiveness of the limited on-chip cache. Aimed at improving the effectiveness of the cache by mitigating the irregular data accesses, prior studies often employ the vertex tiling techniques used in traditional graph processing applications. While being effective at enhan…

Code Generation for High-Level Synthesis of Multiresolution Applications on FPGAs

Multiresolution Analysis (MRA) is a mathematical method that is based on working on a problem at different scales. One of its applications is medical imaging where processing at multiple scales, based on the concept of Gaussian and Laplacian image pyramids, is a well-known technique. It is often applied to reduce noise while preserving image detail on different levels of granularity without modifying the filter kernel. In scientific computing, multigrid methods are a popular choice, as they are asymptotically optimal solvers for elliptic Partial Differential Equations (PDEs). As such algorithm…

编译器支持的降精度和AoS-SoA转换用于异构硬件

Compiler-supported reduced precision and AoS-SoA transformations for heterogeneous hardware

本研究通过编译器支持的降精度和AoS-SoA转换优化异构硬件上的粒子模拟性能，探讨了不同GPU平台下的加速效果与转换策略。