ACM Sigplan Notices最新文献

英文中文

Outcomes of Endoscopic Drainage in Children with Pancreatic Fluid Collections: A Systematic Review and Meta-Analysis. 胰液积聚儿童的内镜引流效果：系统回顾与元分析》。

IF 1.3 Q1 Computer Science

ACM Sigplan Notices

Pub Date : 2022-05-01 Epub Date: 2022-05-09 DOI: 10.5223/pghn.2022.25.3.251

Zaheer Nabi, Rupjyoti Talukdar, Sundeep Lakhtakia, D Nageshwar Reddy

Purpose: Endoscopic drainage is an established treatment modality for adult patients with pancreatic fluid collections (PFCs). Available data regarding the efficacy and safety of endoscopic drainage in pediatric patients are limited. In this systematic review and meta-analysis, we aimed to analyze the outcomes of endoscopic drainage in children with PFCs.

Methods: A literature search was performed in Embase, PubMed, and Google Scholar for studies on the outcomes of endoscopic drainage with or without endoscopic ultrasonography (EUS) guidance in pediatric patients with PFCs from inception to May 2021. The study's primary objective was clinical success, defined as resolution of PFCs. The secondary outcomes included technical success, adverse events, and recurrence rates.

Results: Fourteen studies (187 children, 70.3% male) were included in this review. The subtypes of fluid collection included pseudocysts (60.3%) and walled-off necrosis (39.7%). The pooled technical success rates in studies where drainage of PFCs were performed with and without EUS guidance were 95.3% (95% confidence interval [CI], 89.6-98%; I ²=0) and 93.9% (95% CI, 82.6-98%; I ²=0), respectively. The pooled clinical success after one and two endoscopic interventions were 88.7% (95% CI, 82.7-92.9%; I ²=0) and 92.3% (95% CI, 87.4-95.4%; I ²=0), respectively. The pooled rate of major adverse events was 6.3% (95% CI, 3.3-11.4%; I ²=0). The pooled rate of recurrent PFCs after endoscopic drainage was 10.4% (95% CI, 6.1-17.1%; I ²=0).

Conclusion: Endoscopic drainage is safe and effective in children with PFCs. However, future studies are required to compare endoscopic and EUS-guided drainage of PFCs in children.

目的：内窥镜引流术是治疗胰腺积液（PFC）成人患者的一种成熟治疗方式。有关内镜引流术在儿童患者中的有效性和安全性的现有数据非常有限。在这篇系统综述和荟萃分析中，我们旨在分析内镜引流术在儿童胰腺积液患者中的疗效：方法：我们在 Embase、PubMed 和 Google Scholar 上检索了从开始到 2021 年 5 月期间有关在内镜超声波（EUS）引导下或不在内镜超声波（EUS）引导下对 PFCs 儿童患者进行内镜引流的结果的文献。研究的首要目标是临床成功，即 PFCs 的消退。次要结果包括技术成功率、不良事件和复发率：本次研究共纳入 14 项研究（187 名儿童，70.3% 为男性）。积液的亚型包括假性囊肿（60.3%）和贴壁坏死（39.7%）。在 EUS 引导和无 EUS 引导的情况下进行 PFCs 引流的研究中，汇总的技术成功率分别为 95.3%（95% 置信区间 [CI]，89.6-98%；I 2=0）和 93.9%（95% 置信区间，82.6-98%；I 2=0）。经过一次和两次内镜干预后，汇总的临床成功率分别为88.7%（95% CI，82.7-92.9%；I 2=0）和92.3%（95% CI，87.4-95.4%；I 2=0）。主要不良事件的汇总发生率为 6.3% (95% CI, 3.3-11.4%; I 2=0)。内镜引流术后PFCs复发的汇总率为10.4% (95% CI, 6.1-17.1%; I 2=0)：结论：内镜下引流术对PFCs患儿安全有效。结论：内镜引流对儿童 PFCs 患者安全有效，但还需在今后的研究中对内镜和 EUS 引导下的儿童 PFCs 引流进行比较。

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引用次数: 0

Letter from the Chair 主席的信

Q1 Computer Science

ACM Sigplan Notices

Pub Date : 2020-09-01 DOI: 10.1109/ipc47351.2020.9252541

R. Cytron

引用次数: 0

SEIS 六

Q1 Computer Science

ACM Sigplan Notices

Pub Date : 2019-10-07 DOI: 10.1145/344283.344294

Li Yingjun, Lu Jian

Predominant industrial practice has evolved from general-purpose class libraries to domain-specific frameworks" and design patterns. Both of them are a means to achieve large-scale reuse by capturing successful software development strategies within a particular context. Design patterns focus on reuse of recurring architectural design themes and mainly consist of predefined design structures that can be used as building blocks to compose the architecture of software system. Together the patterns in a specific domain form a pattern language, which can be used to approach a certain class of problems in the application domain. In this paper, we propose a pattern language SEIS++, a set of design patterns, for seismic tool construction and integration in oil and gas exploration domain. The language uses Tools and Materials as the new design conception to guide domain-specific application development, and to enhance software architecture reusability.

引用次数: 1

Proceedings of the 11th ACM SIGPLAN International Symposium on Haskell, Haskell@ICFP 2018, St. Louis, MO, USA, September 27-17, 2018 第11届ACM SIGPLAN Haskell国际研讨会论文集，Haskell@ICFP 2018，美国密苏里州圣路易斯，2018年9月27日至17日

Q1 Computer Science

ACM Sigplan Notices

Pub Date : 2018-12-07 DOI: 10.1145/3299711

引用次数: 0

Proceedings of the 19th ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems, LCTES 2018, Philadelphia, PA, USA, June 19-20, 2018 第19届ACM SIGPLAN/SIGBED嵌入式系统语言、编译器和工具国际会议论文集，LCTES 2018，费城，宾夕法尼亚州，美国，2018年6月19-20日

Q1 Computer Science

ACM Sigplan Notices

Pub Date : 2018-12-07 DOI: 10.1145/3299710

引用次数: 0

Proceedings of the 2018 ACM SIGPLAN International Symposium on Memory Management, ISMM 2018, Philadelphia, PA, USA, June 18, 2018 2018 ACM SIGPLAN内存管理国际研讨会论文集，ISMM 2018，美国费城，2018年6月18日

Q1 Computer Science

ACM Sigplan Notices

Pub Date : 2018-12-07 DOI: 10.1145/3299706

引用次数: 0

Demon 恶魔

Q1 Computer Science

ACM Sigplan Notices

Pub Date : 2018-12-01 DOI: 10.1145/3296975.3186416

Yu Xu, Jianguo Yao, Yaozu Dong, Kun Tian, Xiao Zheng, Haibing Guan

Memory Management Units (MMUs) for on-device address translation are widely used in modern devices. However, conventional solutions for on-device MMU virtualization, such as shadow page table implemented in mediated pass-through, still suffer from high complexity and low performance. We present Demon, an efficient solution for on-DEvice MMU virtualizatiON in mediated pass-through. The key insight is that Demon takes advantage of IOMMU to construct a two-dimensional address translation and dynamically switches the 2nd-dimensional page table to a proper candidate when the device owner switches. In order to support fine-grained parallelism for the device with multiple engines, we put forward a hardware proposal that separates the address space of each engine and enables simultaneous device address remapping for multiple virtual machines (VMs). We implement Demon with a prototype named gDemon which virtualizes Intel GPU MMU. Nonetheless, Demon is not limited to this particular case. Evaluations show that gDemon provides up to 19.73x better performance in the media transcoding workloads and achieves performance improvement of up to 17.09% and 13.73% in the 2D benchmarks and 3D benchmarks, respectively, compared with gVirt. The current release of gDemon scales up to 6 VMs with moderate performance in our experiments. In addition, gDemon simplifies the implementation of GPU MMU virtualization with 37% code reduction.

用于设备上地址转换的内存管理单元(mmu)在现代设备中被广泛使用。然而，设备上MMU虚拟化的传统解决方案，例如在中介传递中实现的影子页表，仍然存在高复杂性和低性能的问题。我们提出了Demon，一种有效的解决方案，用于设备上MMU虚拟化的中介传递。关键的见解是，Demon利用IOMMU构造二维地址转换，并在设备所有者切换时动态地将第二维页表切换到适当的候选页表。为了支持具有多个引擎的设备的细粒度并行性，我们提出了一种硬件方案，该方案将每个引擎的地址空间分开，并允许多个虚拟机(vm)同时进行设备地址重新映射。我们使用一个名为gDemon的原型来实现Demon，它虚拟化了英特尔GPU MMU。尽管如此，Demon并不局限于这种特殊情况。评估表明，gDemon在媒体转码工作负载上的性能比gVirt提高了19.73倍，在2D基准测试和3D基准测试中，性能分别提高了17.09%和13.73%。在我们的实验中，当前版本的gDemon可以扩展到6个虚拟机，性能适中。此外，gDemon简化了GPU MMU虚拟化的实现，减少了37%的代码。

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引用次数: 0

gMig

Q1 Computer Science

ACM Sigplan Notices

Pub Date : 2018-12-01 DOI: 10.1145/3296975.3186414

Jiacheng Ma, Xiao Zheng, Yaozu Dong, Wentai Li, Zhengwei Qi, Bingsheng He, Haibing Guan

This paper introduces gMig, an open-source and practical GPU live migration solution for full virtualization. By taking advantage of the dirty pattern of GPU workloads, gMig presents the One-Shot Pre-Copy combined with the hashing based Software Dirty Page technique to achieve efficient GPU live migration. Particularly, we propose three approaches for gMig: 1) Dynamic Graphics Address Remapping, which parses and manipulates GPU commands to adjust the address mapping to adapt to a different environment after migration, 2) Software Dirty Page, which utilizes a hashing based approach to detect page modification, overcomes the commodity GPU's hardware limitation, and speeds up the migration by only sending the dirtied pages, 3) One-Shot Pre-Copy, which greatly reduces the rounds of pre-copy of graphics memory. Our evaluation shows that gMig achieves GPU live migration with an average downtime of 302 ms on Windows and 119 ms on Linux. With the help of Software Dirty Page, the number of GPU pages transferred during the downtime is effectively reduced by 80.0%.

本文介绍了gMig，一个开源和实用的GPU全虚拟化实时迁移解决方案。通过利用GPU工作负载的脏模式，gMig提出了一次性预拷贝与基于哈希的软件脏页技术相结合，实现高效的GPU实时迁移。具体来说，我们提出了三种gMig方法:1)动态图形地址重新映射(Dynamic Graphics Address Remapping)，通过解析和操作GPU命令来调整地址映射，以适应迁移后的不同环境;2)软件脏页(Software Dirty Page)，利用基于哈希的方法来检测页面修改，克服了商品GPU的硬件限制，只发送脏页，加快了迁移速度;3)一次性预拷贝(One-Shot Pre-Copy)，大大减少了图形内存的预拷贝次数。我们的评估表明，gMig实现GPU实时迁移，在Windows上平均停机时间为302毫秒，在Linux上为119毫秒。借助软件脏页，在停机期间传输的GPU页面数量有效减少了80.0%。

引用次数: 2

FCatch

Q1 Computer Science

ACM Sigplan Notices

Pub Date : 2018-11-30 DOI: 10.1145/3296957.3177161

Haopeng Liu, Xu Wang, Guangpu Li, Shan Lu, Feng Ye, Chen Tian

It is crucial for distributed systems to achieve high availability. Unfortunately, this is challenging given the common component failures (i.e., faults). Developers often cannot anticipate all the timing conditions and system states under which a fault might occur, and introduce time-of-fault (TOF) bugs that only manifest when a node crashes or a message drops at a special moment. Although challenging, detecting TOF bugs is fundamental to developing highly available distributed systems. Unlike previous work that relies on fault injection to expose TOF bugs, this paper carefully models TOF bugs as a new type of concurrency bugs, and develops FCatch to automatically predict TOF bugs by observing correct execution. Evaluation on representative cloud systems shows that FCatch is effective, accurately finding severe TOF bugs.

分布式系统实现高可用性是至关重要的。不幸的是，考虑到常见的组件故障(即错误)，这是具有挑战性的。开发人员通常无法预测可能发生故障的所有时间条件和系统状态，并引入故障时间(TOF)错误，这些错误仅在节点崩溃或在特定时刻丢失消息时才会出现。尽管具有挑战性，但检测TOF错误是开发高可用性分布式系统的基础。与以往的工作依赖于错误注入来暴露TOF错误不同，本文将TOF错误仔细地建模为一种新型的并发错误，并开发了FCatch，通过观察正确执行来自动预测TOF错误。对代表性云系统的评估表明，FCatch是有效的，可以准确地发现严重的TOF漏洞。

引用次数: 3

Automatic Matching of Legacy Code to Heterogeneous APIs 遗留代码与异构api的自动匹配

Q1 Computer Science

ACM Sigplan Notices

Pub Date : 2018-11-30 DOI: 10.1145/3296957.3173182

Philip Ginsbach, Toomas Remmelg, Michel Steuwer, Bruno Bodin, Christophe Dubach, M. O’Boyle

Heterogeneous accelerators often disappoint. They provide the prospect of great performance, but only deliver it when using vendor specific optimized libraries or domain specific languages. This requires considerable legacy code modifications, hindering the adoption of heterogeneous computing. This paper develops a novel approach to automatically detect opportunities for accelerator exploitation. We focus on calculations that are well supported by established APIs: sparse and dense linear algebra, stencil codes and generalized reductions and histograms. We call them idioms and use a custom constraint-based Idiom Description Language (IDL) to discover them within user code. Detected idioms are then mapped to BLAS libraries, cuSPARSE and clSPARSE and two DSLs: Halide and Lift. We implemented the approach in LLVM and evaluated it on the NAS and Parboil sequential C/C++ benchmarks, where we detect 60 idiom instances. In those cases where idioms are a significant part of the sequential execution time, we generate code that achieves 1.26x to over 20x speedup on integrated and external GPUs.

异质加速器常常令人失望。它们提供了出色的性能，但只有在使用特定于供应商的优化库或特定于领域的语言时才能实现。这需要对遗留代码进行大量修改，从而阻碍了异构计算的采用。本文提出了一种自动检测加速器开发机会的新方法。我们将重点关注那些由已建立的api很好地支持的计算:稀疏和密集线性代数、模板代码、广义约简和直方图。我们称它们为习惯用语，并使用基于自定义约束的习惯用语描述语言(IDL)在用户代码中发现它们。然后将检测到的习惯用法映射到BLAS库cuSPARSE和clSPARSE以及两个dsl: Halide和Lift。我们在LLVM中实现了该方法，并在NAS和Parboil顺序C/ c++基准测试中对其进行了评估，其中我们检测到了60个习语实例。在这些情况下，习语是顺序执行时间的重要组成部分，我们生成的代码在集成和外部gpu上实现了1.26倍到20倍以上的加速。

引用次数: 1

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