Search Results - General and reference Cross-computing tools and techniques Performance

MLPerf Inference Benchmark by Reddi, Vijay Janapa, Cheng, Christine, Kanter, David, Mattson, Peter, Schmuelling, Guenther, Wu, Carole-Jean, Anderson, Brian, Breughe, Maximilien, Charlebois, Mark, Chou, William, Chukka, Ramesh, Coleman, Cody, Davis, Sam, Deng, Pan, Diamos, Greg, Duke, Jared, Fick, Dave, Gardner, J. Scott, Hubara, Itay, Idgunji, Sachin, Jablin, Thomas B., Jiao, Jeff, John, Tom St, Kanwar, Pankaj, Lee, David, Liao, Jeffery, Lokhmotov, Anton, Massa, Francisco, Meng, Peng, Micikevicius, Paulius, Osborne, Colin, Pekhimenko, Gennady, Rajan, Arun Tejusve Raghunath, Sequeira, Dilip, Sirasao, Ashish, Sun, Fei, Tang, Hanlin, Thomson, Michael, Wei, Frank, Wu, Ephrem, Xu, Lingjie, Yamada, Koichi, Yu, Bing, Yuan, George, Zhong, Aaron, Zhang, Peizhao, Zhou, Yuchen

“… Over 100 organizations are building ML inference chips, and the systems that incorporate existing models span at least three orders of magnitude in power consumption and five orders of magnitude in performance…”
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InvisiSpec: making speculative execution invisible in the cache hierarchy by Yan, Mengjia, Choi, Jiho, Skarlatos, Dimitrios, Morrison, Adam, Fletcher, Christopher W., Torrellas, Josep

“…Hardware speculation offers a major surface for micro-architectural covert and side channel attacks. Unfortunately, defending against speculative execution…”
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GraFBoost: Using Accelerated Flash Storage for External Graph Analytics by Jun, Sang-Woo, Wright, Andy, Zhang, Sizhuo, Xu, Shuotao, Arvind

“… We compare the performance of GraFBoost with 1 GB of DRAM against various state-of-the-art graph analytics software including FlashGraph, running on a 32-thread Xeon server with 128 GB of DRAM…”
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Bubble-up: Increasing utilization in modern warehouse scale computers via sensible co-locations by Mars, Jason, Lingjia Tang, Hundt, Robert, Skadron, Kevin, Soffa, Mary Lou

“…As much of the world's computing continues to move into the cloud, the overprovisioning of computing resources to ensure the performance isolation of latency-sensitive tasks, such as web search…”
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FireSim: FPGA-Accelerated Cycle-Exact Scale-Out System Simulation in the Public Cloud by Karandikar, Sagar, Mao, Howard, Kim, Donggyu, Biancolin, David, Amid, Alon, Lee, Dayeol, Pemberton, Nathan, Amaro, Emmanuel, Schmidt, Colin, Chopra, Aditya, Huang, Qijing, Kovacs, Kyle, Nikolic, Borivoje, Katz, Randy, Bachrach, Jonathan, Asanovic, Krste

“… designs with a scalable, distributed network simulation. Unlike prior FPGA-accelerated simulation tools, FireSim runs on Amazon EC2 F1, a public cloud FPGA platform…”
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FLIN: Enabling Fairness and Enhancing Performance in Modern NVMe Solid State Drives by Tavakkol, Arash, Sadrosadati, Mohammad, Ghose, Saugata, Kim, Jeremie, Luo, Yixin, Wang, Yaohua, Mansouri Ghiasi, Nika, Orosa, Lois, Gomez-Luna, Juan, Mutlu, Onur

“… Unfortunately, while the elimination of the OS software stack leads to a significant performance improvement, we show in this paper that it introduces a new problem: unfairness…”
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Profiling a warehouse-scale computer by Kanev, Svilen, Darago, Juan Pablo, Hazelwood, Kim, Ranganathan, Parthasarathy, Moseley, Tipp, Wei, Gu-Yeon, Brooks, David

“… microarchitecture becomes ever more important in order to extract maximum performance out of server hardware…”
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Neural Acceleration for General-Purpose Approximate Programs by Esmaeilzadeh, H., Sampson, A., Ceze, L., Burger, D.

“…This paper describes a learning-based approach to the acceleration of approximate programs. We describe the \emph{Parrot transformation}, a program…”
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SLEMI: Equivalence Modulo Input (EMI) Based Mutation of CPS Models for Finding Compiler Bugs in Simulink by Chowdhury, Shafiul Azam, Shrestha, Sohil Lal, Johnson, Taylor T., Csallner, Christoph

“… To provide EMI-based mutation for differential testing of cyber-physical system (CPS) development tools, this paper develops several novel mutation techniques…”
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GPA: A GPU Performance Advisor Based on Instruction Sampling by Zhou, Keren, Meng, Xiaozhu, Sai, Ryuichi, Mellor-Crummey, John

“… Existing performance tools only provide coarse-grained tuning advice at the kernel level…”
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Amber: enabling precise full-system simulation with detailed modeling of all SSD resources by Gouk, Donghyun, Kwon, Miryeong, Zhang, Jie, Koh, Sungjoon, Choi, Wonil, Kim, Nam Sung, Kandemir, Mahmut, Jung, Myoungsoo

“…SSDs become a major storage component in modern memory hierarchies, and SSD research demands exploring future simulation-based studies by integrating SSD…”
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OptiWISE: Combining Sampling and Instrumentation for Granular CPI Analysis by Guo, Yuxin, Chadwick, Alex W., Erdos, Marton, Bora, Utpal, Vougioukas, Ilias, Gabrielli, Giacomo, Jones, Timothy M.

“… Existing profiling tools typically either sample hardware performance counters or instrument the program with extra instructions to analyze its execution…”
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White-Box Performance-Influence Models: A Profiling and Learning Approach by Weber, Max, Apel, Sven, Siegmund, Norbert

“…Many modern software systems are highly configurable, allowing the user to tune them for performance…”
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Flip-N-Write: a simple deterministic technique to improve PRAM write performance, energy and endurance by Cho, Sangyeun, Lee, Hyunjin

“… This paper proposes and evaluates Flip-N-Write, a simple microarchitectural technique to replace a PRAM write operation with a more efficient read-modify-write operation…”
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Predicting defects using network analysis on dependency graphs by Zimmermann, T., Nagappan, N.

“…In software development, resources for quality assurance are limited by time and by cost. In order to allocate resources effectively, managers need to rely on…”
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Performance Analysis with Bayesian Inference by Couderc, Noric, Reichenbach, Christoph, Soderberg, Emma

“…Statistics are part of any empirical science, and performance analysis is no exception…”
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White-Box Performance-Influence Models: A Profiling and Learning Approach (Replication Package) by Weber, Max, Apel, Sven, Siegmund, Norbert

“… Specifically, we describe the general steps and tools that we have used to implement our approach, the data we have obtained, and the evaluation setup…”
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Application-transparent near-memory processing architecture with memory channel network by Alian, Mohammad, Min, Seung Won, Asgharimoghaddam, Hadi, Dhar, Ashutosh, Wang, Dong Kai, Roewer, Thomas, McPadden, Adam, O'Halloran, Oliver, Chen, Deming, Xiong, Jinjun, Kim, Daehoon, Hwu, Wen-mei, Kim, Nam Sung

“…The physical memory capacity of servers is expected to increase drastically with the deployment of the forthcoming non-volatile memory technologies. This is a…”
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Performance Characterization of Popular DNN Models on Out-of-Order CPUs by Prieto, Pablo, Abad, Pablo, Gregorio, Jose Angel, Puente, Valentin

“… However, the ubiquity of DNN models is rapidly extending the presence of this software to general-purpose CPUs…”
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Exascale Deep Learning for Climate Analytics by Kurth, Thorsten, Treichler, Sean, Romero, Joshua, Mudigonda, Mayur, Luehr, Nathan, Phillips, Everett, Mahesh, Ankur, Matheson, Michael, Deslippe, Jack, Fatica, Massimiliano, Prabhat, Prabhat, Houston, Michael

“…We extract pixel-level masks of extreme weather patterns using variants of Tiramisu and DeepLabv3+ neural networks. We describe improvements to the software…”
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