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Accelerating Deep Learning based Identification of Chromatin Accessibility from noisy ATAC-seq Data 36

Accelerating Deep Learning based Identification of Chromatin...

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36th ieee international parallel and distributed processing symposium (ieee IPDPS)

作者： Chaudhary, Narendra Misra, Sanchit Kalamkar, Dhiraj Heinecke, Alexander Georganas, Evangelos Ziv, Barukh Adelman, Menachem Kaul, Bharat Intel Parallel Comp Lab Bangalore Karnataka India Intel Corp Parallel Comp Lab Santa Clara CA USA Intel Corp Haifa Israel

ISBN: (纸本)9781665497473

Identifying accessible chromatin regions is a fundamental problem in epigenomics with ATAC-seq being a commonly used assay. Exponential rise in ATAC-seq experiments has made it critical to accelerate processing of ATAC-seq data that can have a low signal-to-noise ratio for various reasons including low coverage or low cell count. To denoise and identify accessible chromatin regions from noisy ATAC-seq data, use of deep learning on 1D data - using large filter sizes, long tensor widths, and/or dilation - has recently been proposed. Convolutions over 1D data consume a majority of the runtime in these methods. However, existing implementations of the 1D convolution layer for CPUs and GPUs fail to efficiently use the underlying architecture especially in the case of large filter sizes, long tensor widths, and dilation. Here, we present ways to accelerate the end-toend training performance of these deep learning based methods. We evaluate our approach on the recently released AtacWorks toolkit using modern CPUs. Compared to AtacWorks running on an Nvidia DGX-1 box with 8 V100 GPUs, we get up to 2.27 x speedup using just 16 CPU sockets. To achieve this, we build an efficient 1D dilated convolution layer and demonstrate reduced precision (BFloatl6) training and nearly linear scaling from 1 to 16 sockets.

关键词： chromatin accessibility ATAC-seq single cell analysis deep learning 1D convolutions architecture-aware optimizations SIMD BF16

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A2FL: Autonomous and Adaptive File Layout in HPC through Real-time Access Pattern Analysis 38

A2FL: Autonomous and Adaptive File Layout in HPC through Rea...

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international parallel and distributed processing symposium (IPDPS)

作者： Sung, Dong Kyu Son, Yongseok Sim, Alex Wu, Kesheng Byna, Suren Tang, Houjun Eom, Hyeonsang Kim, Changjong Kim, Sunggon Seoul Natl Univ Seoul South Korea Chung Ang Univ Seoul South Korea Lawrence Berkeley Natl Lab Berkeley CA USA Ohio State Univ Columbus OH USA Seoul Natl Univ Sci & Technol Seoul South Korea

ISBN: (纸本)9798350387117;9798350387124

Various scientific applications with different I/O characteristics are executed in HPC systems. However, underlying parallel file systems are unaware of these characteristics of applications, and using a single fixed file layout for all applications can degrade the performance of HPC systems. In this paper, we propose A2FL, an autonomous and adaptive file layout adjustment scheme that optimizes parallel file system configurations by analyzing the access pattern of the applications. The key steps of A2FL are as follows: (1) A2FL initially intercepts the I/O operations of the application, recording their access patterns in real-time. (2) The access patterns are then transformed into a graphical representation used for predicting I/O performance and providing adjustment recommendations. (3) A2FL autonomously adjusts the file layout based on the prediction results, delivering an optimal file layout within the parallel file system. Moreover, we propose A2FL-Compound which analyzes an access pattern by dividing it into smaller components to optimize the file layout in a fine-grained manner. Our evaluations demonstrate that A2FL significantly enhances I/O performance, with improvements of up to 65.9x compared to the default file layout.

关键词： HPC I/O Access Pattern Machine Learning Storage parallel File System

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distributed Sparse Random Projection Trees for Constructing K-Nearest Neighbor Graphs 37

Distributed Sparse Random Projection Trees for Constructing ...

引用

37th ieee international parallel and distributed processing symposium (IPDPS)

作者： Ranawaka, Isuru Rahmant, Md Khaledur Azad, Ariful Indiana Univ Bloomington IN 47405 USA Meta Inc Menlo Pk CA USA

ISBN: (纸本)9798350337662

A random projection tree that partitions data points by projecting them onto random vectors is widely used for approximate nearest neighbor search in high-dimensional space. We consider a particular case of random projection trees for constructing a k-nearest neighbor graph (KNNG) from highdimensional data. We develop a distributed-memory Random Projection Tree (DRPT) algorithm for constructing sparse random projection trees and then running a query on the forest to create the KNN graph. DRPT uses sparse matrix operations and a communication reduction scheme to scale KNN graph constructions to thousands of processes on a supercomputer. The accuracy of DRPT is comparable to state-of-the-art methods for approximate nearest neighbor search, while it runs two orders of magnitude faster than its peers. DRPT is available at https://***/HipGraph/DRPT.

关键词： distributed memory algorithms k nearest neighbor graph parallel algorithm

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distributed Multi-GPU Community Detection on Exascale Computing Platforms

Distributed Multi-GPU Community Detection on Exascale Comput...

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1st international Conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Sattar, Naw Safrin Lu, Hao Wang, Feiyi Halappanavar, Mahantesh Oak Ridge Natl Lab Oak Ridge TN 37830 USA Pacific Northwest Natl Lab Richland WA 99352 USA

ISBN: (纸本)9798350364613;9798350364606

Community detection is a fundamental operation in graph mining, and by uncovering hidden structures and patterns within complex systems it helps solve fundamental problems pertaining to social networks, such as information diffusion, epidemics, and recommender systems. Scaling graph algorithms for massive networks becomes challenging on modern distributed-memory multi-GPU (Graphics processing Unit) systems due to limitations such as irregular memory access patterns, load imbalances, higher communication-computation ratios, and cross-platform support. We present a novel algorithm HiPDPL-GPU (distributed parallel Louvain) to address these challenges. We conduct experiments involving different partitioning techniques to achieve an optimized performance of HiPDPL-GPU on the two largest supercomputers: Frontier and Summit. Remarkably, HiPDPL-GPU processes a graph with 4.2 billion edges in less than 3 minutes using 1024 GPUs. Qualitatively, the performance of HiPDPL-GPU is similar or better compared to other state-of-the-art CPU- and GPU-based implementations. While prior GPU implementations have predominantly employed CUDA, our first-of-its-kind implementation for community detection is cross-platform, accommodating both AMD and NVIDIA GPUs.

关键词： multi-GPU Louvain community detection clustering hybrid HIP MPI

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DeepThermo: Deep Learning Accelerated parallel Monte Carlo Sampling for Thermodynamics Evaluation of High Entropy Alloys 37

DeepThermo: Deep Learning Accelerated Parallel Monte Carlo S...

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37th ieee international parallel and distributed processing symposium (IPDPS)

作者： Yin, Fungi Wang, Feiyi Shankar, Mallikarjun (Arjun) Oak Ridge Natl Lab Oak Ridge TN 37830 USA

ISBN: (纸本)9798350337662

Since the introduction of Metropolis Monte Carlo (MC) sampling, it and its variants have become standard tools used for thermodynamics evaluations of physical systems. However, a long-standing problem that hinders the effectiveness and efficiency of MC sampling is the lack of a generic method (a.k.a. MC proposal) to update the system configurations. Consequently, current practices are not scalable. Here we propose a parallel MC sampling framework for thermodynamics evaluation-DeepThermo. By using deep learning-based MC proposals that can globally update the system configurations, we show that DeepThermo can effectively evaluate the phase transition behaviors of high entropy alloys, which have an astronomical configuration space. For the first time, we directly evaluate a density of states expanding over a range of similar to e(10,000) for a real material. We also demonstrate DeepThermo's performance and scalability up to 3,000 GPUs on both NVIDIA V100 and AMD MI250X-based supercomputers.

关键词： Monte Carlo Method Deep Learning High Entropy Alloy

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Accelerating Native Transaction processing in LSM-Based Persistent Key-Value Stores

Accelerating Native Transaction Processing in LSM-Based Pers...

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1st international Conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Xue, Jin Shao, Zili Chinese Univ Hong Kong Dept Comp Sci & Engn Hong Kong Peoples R China

ISBN: (纸本)9798350364613;9798350364606

In recent years, key-value stores (KV stores) [1]-[3] begin to gain popularity as storage engines for large-scale data applications. KV stores are fundamentally different from traditional SQL databases and with the key-value data model, they have various advantages, such as ease of use, flexibility and higher performance. However, some essential features in SQL databases, most notably atomicity, consistency, isolation and durability (ACID) transaction processing [4], are considered impractical and thus are generally not included in a KV store design. With the recent advancement, these features begin to be integrated into KV stores, giving birth to a brand new class of database management systems called NewSQL databases [5], [6]. The rationale behind NewSQL databases is that with ACID transaction support, KV stores can serve as storage engines for an upper-layer SQL query processor to handle SQL queries [7]-[9]. In this way, NewSQL databases can provide both the scalability of KV stores and the ACID guarantees required for online transaction processing (OLTP), thus providing high performance for different types of workloads in a large-scale data system. © 2024 ieee.

关键词： key-value store log-structured merge tree transaction processing

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Least Squares on GPUs in Multiple Double Precision 36

Least Squares on GPUs in Multiple Double Precision

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36th ieee international parallel and distributed processing symposium (ieee IPDPS)

作者： Verschelde, Jan Univ Illinois Dept Math Stat & Comp Sci 851 S Morgan StM-C 249 Chicago IL 60607 USA

ISBN: (纸本)9781665497473

This paper describes the application of the code generated by the CAMPARY software to accelerate the solving of linear systems in the least squares sense on Graphics processing Units (GPUs), in double double, quad double, and octo double precision. The goal is to use accelerators to offset the cost overhead caused by multiple double precision arithmetic. For the blocked Householder QR and the back substitution, of interest are those dimensions at which teraflop performance is attained. The other interesting question is the cost overhead factor that appears each time the precision is doubled. Experimental results are reported on five different NVIDIA GPUs, with a particular focus on the P100 and the V100, both capable of teraflop performance. Thanks to the high Compute to Global Memory Access (CGMA) ratios of multiple double arithmetic, teraflop performance is already attained running the double double QR on 1,024-by-1,024 matrices, both on the P100 and the V100. For the back substitution, the dimension of the upper triangular system must be as high as 17,920 to reach one teraflops on the V100, in quad double precision, and then taking only the times spent by the kernels into account. The lower performance of the back substitution in small dimensions does not prevent teraflop performance of the solver at dimension 1,024, as the time for the QR decomposition dominates. In doubling the precision from double double to quad double and from quad double to octo double, the observed cost overhead factors are lower than the factors predicted by the arithmetical operation counts. This observation correlates with the increased performance for increased precision, which can again be explained by the high CGMA ratios.

关键词： acceleration back substitution blocked Householder QR Graphics processing Unit (GPU) least squares multiple double multiprecision

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GraphTensor: Comprehensive GNN-Acceleration Framework for Efficient parallel processing of Massive Datasets 37

GraphTensor: Comprehensive GNN-Acceleration Framework for Ef...

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37th ieee international parallel and distributed processing symposium (IPDPS)

作者： Jang, Junhyeok Kwon, Miryeong Gouk, Donghyun Bae, Hanyeoreum Jung, Myoungsoo Korea Adv Inst Sci & Technol KAIST Comp Architecture & Memory Syst Lab Daejeon South Korea

ISBN: (纸本)9798350337662

We present GraphTensor, a comprehensive opensource framework that supports efficient parallel neural network processing on large graphs. GraphTensor offers a set of easy-to-use programming primitives that appreciate both graph and neural network execution behaviors from the beginning (graph sampling) to the end (dense data processing). Our framework runs diverse graph neural network (GNN) models in a destination-centric, feature-wise manner, which can significantly shorten training execution times in a GPU. In addition, GraphTensor rearranges multiple GNN kernels based on their system hyperparameters in a self-governing manner, thereby reducing the processing dimensionality and the latencies further. From the end-to-end execution viewpoint, GraphTensor significantly shortens the service-level GNN latency by applying pipeline parallelism for efficient graph dataset preprocessing. Our evaluation shows that GraphTensor exhibits 1.4x better training performance than emerging GNN frameworks under the execution of large-scale, real-world graph workloads. For the end-to-end services, GraphTensor reduces training latencies of an advanced version of the GNN frameworks (optimized for multi-threaded graph sampling) by 2.4x, on average.

关键词： graph neural network large-scale graph GPU

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CliZ: Optimizing Lossy Compression for Climate Datasets with Adaptive Fine-tuned Data Prediction 38

CliZ: Optimizing Lossy Compression for Climate Datasets with...

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international parallel and distributed processing symposium (IPDPS)

作者： Jian, Zizhe Di, Sheng Liu, Jinyang Zhao, Kai Liang, Xin Xu, Haiying Underwood, Robert Wu, Shixun Huang, Jiajun Chen, Zizhong Cappello, Franck Univ Calif Riverside Riverside CA USA Argonne Natl Lab Lemont IL USA Florida State Univ Tallahassee FL USA Univ Kentucky Lexington KY USA Natl Ctr Atmospher Res Boulder CO USA

ISBN: (纸本)9798350387117;9798350387124

Benefiting from the cutting-edge supercomputers that support extremely large-scale scientific simulations, climate research has advanced significantly over the past decades. However, new critical challenges have arisen regarding efficiently storing and transferring large-scale climate data among distributed repositories and databases for post hoc analysis. In this paper, we develop CliZ, an efficient online error-controlled lossy compression method with optimized data prediction and encoding methods for climate datasets across various climate models. On the one hand, we explored how to take advantage of particular properties of the climate datasets (such as mask-map information, dimension permutation/fusion, and data periodicity pattern) to improve the data prediction accuracy. On the other hand, CliZ features a novel multi-Huffman encoding method, which can significantly improve the encoding efficiency. Therefore significantly improving compression ratios. We evaluated CliZ versus many other state-of-the-art error-controlled lossy compressors (including SZ3, ZFP, SPERR, and QoZ) based on multiple real-world climate datasets with different models. Experiments show that CliZ outperforms the second-best compressor (SZ3, SPERR, or QoZ1.1) on climate datasets by 20%-200% in compression ratio. CliZ can significantly reduce the data transfer cost between the two remote Globus endpoints by 32%-38%.

关键词： error-controlled lossy compression climate datasets distributed data repository/database

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Shared -Memory parallel Edmonds Blossom Algorithm for Maximum Cardinality Matching in General Graphs

Shared -Memory Parallel Edmonds Blossom Algorithm for Maximu...

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1st international Conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Schwing, Gregory Grosu, Daniel Schwiebert, Loren Wayne State Univ Dept Comp Sci Detroit MI 48202 USA

ISBN: (纸本)9798350364613;9798350364606

The Edmonds Blossom algorithm is implemented here using depth-first search, which is intrinsically serial. By streamlining the code, our serial implementation is consistently three to five times faster than the previously fastest general graph matching code. By extracting parallelism across iterations of the algorithm, with coarse -grain locking, we are able to further reduce the run lime on random regular graphs fourfold and obtain a two-fold reduction of run time on real-world graphs with similar topology. Solving very sparse graphs (average degree less than four) exhibiting comnwnity structure with eight threads led to a slow down of three-fold, but this slow down is replaced by marginal speed up once the average degree is greater than four. We conclude that our parallel coarse -grain locking implementation performs well when extracting parallelism from this augmenting-path-based algorithm and may work well for similar algorithms.

关键词： matching general graphs Edmonds Blossom parallel

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