Speculative data-parallel algorithms for language recognition have been widely experimented for various types of finitestate automata (FA), deterministic (DFA) and nondeterministic (NFA), often derived fromregular exp...
详细信息
ISBN:
(纸本)9798400714436
Speculative data-parallel algorithms for language recognition have been widely experimented for various types of finitestate automata (FA), deterministic (DFA) and nondeterministic (NFA), often derived fromregular expressions (RE). Such an algorithm cuts the input string into chunks, independently recognizes each chunk in parallel by means of identical FAs, and at last joins the chunk results and checks the overall consistency. In chunk recognition, it is necessary to speculatively start the FAs in any state, thus causing an overhead that reduces the speedup over a serial algorithm. the existing data-parallel DFA-based recognizers suffer from an excessive number of starting states, and the NFA-based ones suffer from the number of nondeterministic transitions.
Deep neural networks (DNNs) increasingly rely on parallel structures to enhance performance and efficiency. However, existing machine learning compilers (MLCs) face challenges in optimizing these structures due to lim...
详细信息
ISBN:
(纸本)9798400714436
Deep neural networks (DNNs) increasingly rely on parallel structures to enhance performance and efficiency. However, existing machine learning compilers (MLCs) face challenges in optimizing these structures due to limited parallel fusion scopes and insufficient consideration of intra-operator information. this paper introduces Magneto, a novel framework designed to accelerate parallel structures in DNNs through the co-optimization of parallel operators. By expanding the scope of parallel operator fusion and introducing a dedicated co-tuning algorithm, Magneto unlocks new opportunities for co-optimization. Experimental results demonstrate that Magneto outperforms NVIDIA TensorRT and AMD MIGraphX, achieving speedups of 3.02× and 4.19×, respectively.
Molecular dynamics simulation emerges as an important area that HPC+AI helps to investigate the physical properties, with machine-learning interatomic potentials (MLIPs) being used. General-purpose machine-learning (M...
详细信息
ISBN:
(纸本)9798400714436
Molecular dynamics simulation emerges as an important area that HPC+AI helps to investigate the physical properties, with machine-learning interatomic potentials (MLIPs) being used. General-purpose machine-learning (ML) tools have been leveraged in MLIPs, but they are not perfectly matched with each other, since many optimization opportunities in MLIPs have been missed by ML tools. this inefficiency arises from the fact that HPC+AI applications work with far more computational complexity compared with pure AI scenarios. this paper has developed an MLIP, named TensorMD, independently from any ML tool. TensorMD has been evaluated on two supercomputers and scaled to 51.8 billion atoms, i.e., ~ 3× compared with state-of-the-art.
We present a simple yet effective technique for improving performance of lock-based code using the hardware lock elision (HLE) feature in Intel's upcoming Haswell processor. We also describe how to extend Haswell&...
详细信息
ISBN:
(纸本)9781450319225
We present a simple yet effective technique for improving performance of lock-based code using the hardware lock elision (HLE) feature in Intel's upcoming Haswell processor. We also describe how to extend Haswell's HLE mechanism to achieve a similar effect to our lock elision scheme entirely in hardware.
Recently, graph computation has emerged as an important class of high-performance computing application whose characteristics differ markedly from those of traditional, compute-bound, kernels. Libraries such as BLAS, ...
详细信息
ISBN:
(纸本)9781450319225
Recently, graph computation has emerged as an important class of high-performance computing application whose characteristics differ markedly from those of traditional, compute-bound, kernels. Libraries such as BLAS, LAPACK, and others have been successful in codifying best practices in numerical computing. the data-driven nature of graph applications necessitates a more complex application stack incorporating runtime optimization. In this paper, we present a method of phrasing graph algorithms as collections of asynchronous, concurrently executing, concise code fragments which may be invoked both locally and in remote address spaces. A runtime layer performs a number of dynamic optimizations, including message coalescing, message combining, and software routing. Practical implementations and performance results are provided for a number of representative algorithms.
JavaScript, the most popular language on the Web, is rapidly moving to the server-side, becoming even more pervasive. Still, JavaScript lacks support for shared memory parallelism, making it challenging for developers...
详细信息
ISBN:
(纸本)9781450319225
JavaScript, the most popular language on the Web, is rapidly moving to the server-side, becoming even more pervasive. Still, JavaScript lacks support for shared memory parallelism, making it challenging for developers to exploit multicores present in both servers and clients. In this paper we present TigerQuoll, a novel API and runtime for parallelprogramming in JavaScript. TigerQuoll features an event-based API and a parallel runtime allowing applications to exploit a mutable shared memory space. the programming model of TigerQuoll features automatic consistency and concurrency management, such that developers do not have to deal with shared-data synchronization. TigerQuoll supports an innovative transaction model that allows for eventual consistency to speed up high-contention workloads. Experiments show that TigerQuoll applications scale well, allowing one to implement common parallelism patterns in JavaScript.
暂无评论