作者: 天空 時間: 2025-3-21 21:28 作者: 使混合 時間: 2025-3-22 03:11 作者: 挫敗 時間: 2025-3-22 06:51 作者: Vulnerary 時間: 2025-3-22 11:57 作者: 柔美流暢 時間: 2025-3-22 15:12 作者: 柔美流暢 時間: 2025-3-22 19:42
Bringing Auto-Tuning to?HIP: Analysis of?Tuning Impact and?Difficulty on?AMD and?Nvidia GPUsficiency of these approaches on AMD devices have hardly been studied. This paper aims to address this gap by introducing an auto-tuner for AMD’s HIP. We do so by extending Kernel Tuner, an open-source Python library for auto-tuning GPU programs. We analyze the performance impact and tuning difficult作者: 甜瓜 時間: 2025-3-23 00:36
A Mechanism to?Generate Interception Based Tools for?HPC Libraries behaviour to end users, code developers and system administrators. However, most tools currently do not support performance analysis at the granularity of libraries, which are the most important level of abstraction for code when developing modern applications. To overcome this limitation, we prese作者: 矛盾 時間: 2025-3-23 02:25
OMPGPT: A Generative Pre-trained Transformer Model for?OpenMPent of code-based large language models such as StarCoder, WizardCoder, and CodeLlama, which are trained extensively on vast repositories of code and programming languages. While the generic abilities of these code LLMs are helpful for many programmers in tasks like code generation, the area of high作者: 使殘廢 時間: 2025-3-23 09:28 作者: Spongy-Bone 時間: 2025-3-23 11:51
Light-Weight Prediction for?Improving Energy Consumption in?HPC Platformsjor issue for the High-performance computing (HPC) community. Including reliable energy management to a supercomputer’s resource and job management system (RJMS) is not an easy task. The energy consumption of jobs is rarely known in advance and the workload of every machine is unique and different f作者: 消極詞匯 時間: 2025-3-23 15:33 作者: 工作 時間: 2025-3-23 20:08 作者: 甜食 時間: 2025-3-24 01:30 作者: Fierce 時間: 2025-3-24 04:44
PriCE: Privacy-Preserving and?Cost-Effective Scheduling for?Parallelizing the?Large Medical Image Prl image processing tasks to hybrid clouds has benefits, such as a significant reduction of execution time and monetary cost. However, due to privacy concerns, it is still challenging to process sensitive medical images over clouds, which would hinder their deployment in many real-world applications.作者: 法律 時間: 2025-3-24 09:03 作者: HPA533 時間: 2025-3-24 10:51 作者: 生氣的邊緣 時間: 2025-3-24 15:14
https://doi.org/10.1007/978-3-031-69577-3parallel and distributed computing; programming; compilers; performance; scheduling; resource management; 作者: Herbivorous 時間: 2025-3-24 22:58 作者: Aromatic 時間: 2025-3-25 01:50 作者: Orthodontics 時間: 2025-3-25 06:19
Ramon Puigjaner,Dominique Potierx computations found in deep learning applications. Intel oneAPI’s Explicit SIMD (ESIMD) SYCL extension API allows for simpler vectorization of arithmetic and memory operations which is critical in achieving good performance. We explore sparse matrix operations relevant to deep learning applications作者: 有惡意 時間: 2025-3-25 10:55
https://doi.org/10.1007/978-3-319-33789-0orkloads. However, the benchmark’s representativeness of real-world HPC and AI workloads is unclear. In this paper, we discuss the HPL-MxP benchmark from a numerical perspective and propose new rules and data generation for numerically meaningful comparisons. We present experiments showing that the 作者: hauteur 時間: 2025-3-25 13:26
https://doi.org/10.1007/978-3-658-38618-4xible programmability. Coarse-grained Reconfigurable Arrays (CGRAs) show great potential with their regular parallel architectures and word-level spatio-temporal reconfigurability. However, the mapping of image processing applications on CGRAs faces two main challenges: 1) low-level CGRA programming作者: 宴會 時間: 2025-3-25 16:40 作者: 進(jìn)取心 時間: 2025-3-25 23:49 作者: consolidate 時間: 2025-3-26 04:04 作者: 傳染 時間: 2025-3-26 06:37 作者: 復(fù)習(xí) 時間: 2025-3-26 09:22 作者: jabber 時間: 2025-3-26 13:35
Modeling Uncertainty with Fuzzy Logicjor issue for the High-performance computing (HPC) community. Including reliable energy management to a supercomputer’s resource and job management system (RJMS) is not an easy task. The energy consumption of jobs is rarely known in advance and the workload of every machine is unique and different f作者: Allure 時間: 2025-3-26 17:35 作者: Fluctuate 時間: 2025-3-26 22:20 作者: Headstrong 時間: 2025-3-27 05:04 作者: 母豬 時間: 2025-3-27 08:26 作者: 攤位 時間: 2025-3-27 11:33 作者: 朦朧 時間: 2025-3-27 14:21
https://doi.org/10.1007/978-981-15-9144-0f multi-tenant deep learning workloads. These facilities implement virtual cluster partitioning to maintain isolation across product groups. Dynamically adjusting resource allocation across virtual clusters can effectively enhance resource utilization. However, efficient GPU resource scaling hinges 作者: cajole 時間: 2025-3-27 20:25 作者: tympanometry 時間: 2025-3-28 01:00
Euro-Par 2024: Parallel Processing978-3-031-69577-3Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: 骯臟 時間: 2025-3-28 04:44
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/f/image/320755.jpg作者: 天氣 時間: 2025-3-28 07:14 作者: 使成波狀 時間: 2025-3-28 11:59 作者: 助記 時間: 2025-3-28 17:33 作者: 摘要記錄 時間: 2025-3-28 19:06
https://doi.org/10.1007/b106473d?with better performance, we demonstrate the robustness of our solutions in scenarios where information is limited or inaccurate. This research provides insights?into the trade-offs between the depth of application characterization and?the practicality of scheduling I/O resources.作者: 出處 時間: 2025-3-29 02:59 作者: flex336 時間: 2025-3-29 05:16 作者: 密碼 時間: 2025-3-29 09:41 作者: Pastry 時間: 2025-3-29 15:04 作者: 賠償 時間: 2025-3-29 17:22 作者: 稀釋前 時間: 2025-3-29 21:21
Conference proceedings 2024esource management, cloud, edge computing, and workflows;?..Part II: Architectures and accelerators; data analytics, AI and computational science;?..Part III: Theory and algorithms; multidisciplinary, domain-specific and applied parallel and distributed computing..作者: moribund 時間: 2025-3-30 01:31 作者: 軍火 時間: 2025-3-30 04:13 作者: Reservation 時間: 2025-3-30 11:40 作者: 國家明智 時間: 2025-3-30 15:38
https://doi.org/10.1007/978-3-642-31000-3ntegrity of Redis data structure. The new approach brings up to 1.38. average speedup for the key-value retrieval process, and significantly reduces misses in TLB and last-level cache. It outperforms SLB, an address caching software approach and has match the performance to STLT, a software-hardware co-designed address-centric design.作者: 邊緣帶來墨水 時間: 2025-3-30 19:54
https://doi.org/10.1007/978-1-4471-2094-0. by associating it to a Multiple Subset Sum problem. Our algorithm is an improvement over the existing literature, which provides a (.) approximation for scheduling with arbitrary rejection costs. We evaluate and discuss the effectiveness of our approach through a series of experiments, comparing it to existing algorithms.作者: 特別容易碎 時間: 2025-3-31 00:05
Deconstructing HPL-MxP Benchmark: A?Numerical Perspectivetter specify these requirements for numerical formats to produce comparable performance numbers, and suggest new input data generation to make it numerically relevant. We validate our proposal on Int8, Int4, and BF16 implementations to demonstrate the numerical significance of the benchmark using our new generator.作者: 小卒 時間: 2025-3-31 03:17 作者: 感染 時間: 2025-3-31 05:57 作者: 富饒 時間: 2025-3-31 12:22
EKRM: Efficient Key-Value Retrieval Method to?Reduce Data Lookup Overhead for?Redisntegrity of Redis data structure. The new approach brings up to 1.38. average speedup for the key-value retrieval process, and significantly reduces misses in TLB and last-level cache. It outperforms SLB, an address caching software approach and has match the performance to STLT, a software-hardware co-designed address-centric design.
