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 PARGEO, a multicore library for computational geometry algorithms. We describe two of the algorithms from PARGEO, convex hull and the smallest enclosing ball, and present a short evaluation of all implement...
详细信息
ISBN:
(纸本)9781450392044
We present PARGEO, a multicore library for computational geometry algorithms. We describe two of the algorithms from PARGEO, convex hull and the smallest enclosing ball, and present a short evaluation of all implementations currently in PARGEO.
We present KUMQUAT, a system for automatically generating data-parallel implementations of UNIX shell commands and pipelines. the generated parallel versions split input streams, execute multiple instantiations of the...
详细信息
ISBN:
(纸本)9781450392044
We present KUMQUAT, a system for automatically generating data-parallel implementations of UNIX shell commands and pipelines. the generated parallel versions split input streams, execute multiple instantiations of the original pipeline commands to process the splits in parallel, then combine the resulting parallel outputs to produce the final output stream. KumQUAT automatically synthesizes the combine operators, with a domain-specific combiner language acting as a strong regularizer that promotes efficient inference of correct combiners. We present experimental results that show that these combiners enable the effective parallelization of our benchmark scripts.
Computing the product of two sparse matrices (SpGEMM) is a fundamental operation in various combinatorial and graph algorithms as well as various bioinformatics and data analytics applications for computing inner-prod...
详细信息
ISBN:
(纸本)9781450392044
Computing the product of two sparse matrices (SpGEMM) is a fundamental operation in various combinatorial and graph algorithms as well as various bioinformatics and data analytics applications for computing inner-product similarities. For an important class of algorithms, only a subset of the output entries are needed, and the resulting operation is known as Masked SpGEMM since a subset of the output entries is considered to be "masked out". In this work, we investigate various novel algorithms and data structures for this rather challenging and important computation, and provide guidelines on how to design a fast Masked-SpGEMM for shared-memory architectures.
the accompanying poster to this short paper presents a combination of reverse mode AD and formal methods to enable efficient differentiation of (or backpropagation through) shared-memory parallel code. Compared to the...
详细信息
ISBN:
(纸本)9781450392044
the accompanying poster to this short paper presents a combination of reverse mode AD and formal methods to enable efficient differentiation of (or backpropagation through) shared-memory parallel code. Compared to the state of the art, our approach can more often avoid the need for atomic updates or private data copies during the parallel derivative computation, even in the presence of unstructured or data-dependent data access patterns. this is achieved by gathering information about the memory access patterns from the input program, which is assumed to be correctly parallelized. this information is then used to build a model of assertions in a theorem prover, which can be used to check the safety of shared memory accesses during the parallel derivative computation.
the emergence of heterogeneous memory (HM) provides a cost-effective and high-performance solution to memory-consuming HPC applications. However, using HM, wisely migrating data objects on it is critical for high perf...
详细信息
ISBN:
(纸本)9781450392044
the emergence of heterogeneous memory (HM) provides a cost-effective and high-performance solution to memory-consuming HPC applications. However, using HM, wisely migrating data objects on it is critical for high performance. In this work, we introduce a load balance-aware page management system, named LB-HM. LB-HM introduces task semantics during memory profiling, rather than being application-agnostic. Evaluating with a set of memory-consuming HPC applications, we show that we show that LB-HM reduces existing load imbalance and leads to an average of 17.1% and 15.4% (up to 26.0% and 23.2%) performance improvement, compared with a hardware-based solution and an industry-quality software-based solution on Optane-based HM.
暂无评论