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Reuse Distance-Based Probabilistic Cache Replacement

This article proposes Probabilistic Replacement Policy (PRP), a novel replacement policy that evicts the line with minimum estimated hit probability... (more)

MINIME-GPU

We introduce MINIME-GPU, a novel automated benchmark synthesis framework for graphics processing units (GPUs) that serves to speed up architectural simulation of modern GPU architectures. Our framework captures important characteristics of original GPU applications and generates synthetic GPU benchmarks using the Open Computing Language (OpenCL)... (more)

Scalable Energy Efficiency with Resilience for High Performance Computing Systems

Ever-growing performance of supercomputers nowadays brings demanding requirements of energy efficiency and resilience, due to rapidly expanding size... (more)

Tumbler

Schedulers used by modern OSs (e.g., Oracle Solaris 11™ and GNU/Linux) balance load by balancing the number of threads in run queues of different cores. While this approach is effective for a single CPU multicore system, we show that it can lead to a significant load imbalance across CPUs of a multi-CPU multicore system. Because different... (more)

Four Metrics to Evaluate Heterogeneous Multicores

Semiconductor device scaling has made single-ISA heterogeneous processors a reality. Heterogeneous processors contain a number of different CPU cores... (more)

SPCM

Phase Change Memory (PCM) devices are one of the known promising technologies to take the place of DRAM devices with the aim of overcoming the obstacles of reducing feature size and stopping ever growing amounts of leakage power. In exchange for providing high capacity, high density, and nonvolatility, PCM Multilevel Cells (MLCs) impose high write... (more)

Two-Level Hybrid Sampled Simulation of Multithreaded Applications

Sampled microarchitectural simulation of single-threaded applications is mature technology for over a decade now. Sampling multithreaded applications,... (more)

Integrated Mapping and Synthesis Techniques for Network-on-Chip Topologies with Express Channels

The addition of express channels to a traditional mesh network-on-chip (NoC) has emerged as a viable... (more)

PARSECSs

In this work, we show how parallel applications can be implemented efficiently using task parallelism. We also evaluate the benefits of such parallel paradigm with respect to other approaches. We use the PARSEC benchmark suite as our test bed, which includes applications representative of a wide... (more)

A Framework for Application-Guided Task Management on Heterogeneous Embedded Systems

In this article, we propose a general framework for fine-grain application-aware task management in heterogeneous embedded platforms, which allows... (more)

Managing Mismatches in Voltage Stacking with CoreUnfolding

Five percent to 25% of power could be wasted before it is delivered to the computational resources on a die, due to inefficiencies of voltage... (more)

FaultSim

As memory systems scale, maintaining their Reliability Availability and Serviceability (RAS) is becoming more complex. To make matters worse, recent studies of DRAM failures in data centers and supercomputer environments have highlighted that large-granularity failures are common in DRAM chips. Furthermore, the move toward 3D-stacked memories can... (more)

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About TACO

The ACM Transactions on Architecture and Code Optimization focuses on hardware, software, and systems research spanning the fields of computer architecture and code optimization. Articles that appear in TACO present new techniques and concepts or report on experiences and experiments with actual systems. Insights useful to computer architects, hardware or software developers, system designers and tool builders are emphasized.

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Forthcoming Articles

R-GPU: a Reconfigurable GPU Architecture

To improve performance and energy efficiency of contemporary GPUs, we introduce R-GPU: a reconfigurable GPU architecture with communicating cores. R-GPU adds the ability to a GPU to reorganize its cores in a reconfigurable network. In R-GPU data movement and control is implicit in the configuration of the network. Each core executes a fixed instruction, reducing instruction decode count and increasing energy efficiency. On a number of benchmarks an average performance improvement of 2.1X is shown. A power estimation shows that power consumption can be reduced by 6%, while area increases by 4%, leading to an energy consumption reduction of 55%.

Optimizing Indirect Branches in Dynamic Binary Translators

A significant factor in the performance of a dynamic binary translator is its handling of indirect branches. An indirect branch requires translating a source program counter address to a translated program counter address every time the branch is executed, can impose a serious runtime penalty if it is not handled efficiently. This paper present three novel techniques to improve the performance of indirect branch translation: predicting function returns using a return address stack, detecting and optimizing branch tables in translated code and an efficient and thread-safe hash table mechanism for generic indirect branch handling.

Examining and Reducing the Influence of Sampling Errors on Feedback-Driven Optimizations

Feedback-driven optimization (FDO) is an important component in mainstream compilers. This paper gives the first systematic study in how sampling rates affect the accuracy of collected profiles and how the accuracy correlates with the usefulness of the profile for modern FDO. By studying basic block and edge profiles for FDO in two mature compilers, it reveals several counter-intuitive observations. Through a detailed analysis, it identifies three types of sampling-caused errors that critically impair the quality of the profiles for FDO. It then introduces a simple way to rectify profiles based on the findings.

A Compiler Approach for Exploiting Partial SIMD Parallelism

Existing vectorization techniques are ineffective for loops that exhibit little loop-level parallelism but some limited superword-level parallelism (SLP). We show that effectively vectorizing such loops requires partial vector operations to be executed correctly and efficiently, where the degree of partial SIMD parallelism is smaller than the SIMD datapath width. We present a simple yet effective SLP compiler technique, called PAVER (PArtial VEctorizeR), to optimize such partially vectorizable loops. For a set of 9 C/C++/Fortran applications with partial SIMD parallelism, PAVER achieves significantly better kernel and whole-program speedups than LLVM on both Intel's AVX and ARM's NEON.

Dynamic Memory Balancing for Virtualization

Allocating memory dynamically for virtual machines (VMs) according to demands provides significant benefits and great challenges. Efficient memory resource management requires knowledge of the memory demands at runtime. To summarize current working set size (WSS) of a VM but also model the relationship between performance and target memory allocation size, a widely proposed approach is to construct a miss ratio curve for a VM, whose cost is nontrivial. This paper first introduces a low cost WSS tracking system with effective optimizations on data structures, we also propose a MEmory Balancer, which dynamically reallocates guest memory based on the predicted WSS.

Hardware Performance Counter-Based Malware Identification and Detection with Adaptive Compressive Sensing

Hardware Performance Counter-based runtime checking is an effective way to identify malicious behaviors of malware, and detect malicious modifications to a legitimate programs control-flow. To reduce the overhead at the monitored platforms which has limited storage and computing resources, we present a sample-locally-analyze-remotely technique. The sampled HPC data is sent to a remote server for checking. To minimize the I/O bandwidth required for transmission, the fine-grained HPC profiles are compressed into much smaller vectors with Compressive Sensing. The experimental results demonstrate an 80% I/O bandwidth reduction after applying Compressive Sensing, without compromising the detection and identification capabilities.

Building heterogeneous UVMs (unified virtual memories) without the overhead

This work proposes a novel scheme to facilitate heterogeneous systems with unified virtual memory. State-of-the-art proposals, implement coherence protocols for sequential consistency (SC) between CPU cores, and between devices. Such mechanisms introduce severe bottlenecks in the system; therefore we adopt the heterogeneous race-free (HRF) memory model. The use of HRF simplifies the coherency protocol and the GPU memory management unit (MMU). Our protocol optimizes CPU and GPU demands separately, with the GPU part being simpler while the CPU being more elaborate and latency-aware. We achieve 35% performance improvement (average) and 50% EDP reduction (25% energy) over the corresponding SC implementation.

Clustering-Based Selection for the Exploration of Compiler Optimization Sequences

Sequence of compiler optimizations can have a significant impact on the application performance achieved. As a vast Design Space Exploration (DSE) is a complex and time consuming task, several researchers investigated strategies to both select good optimization sequences and to reduce the exploration time. Here, we present a clustering-based DSE approach that explores the set resulting from the combination of optimizations previously suggested for clustered functions. Experimental results reveal that our approach achieved a significant reduction on the total exploration time of the search space (20x over a Genetic Algorithm-based approach), and important performance speedups (42% over the baseline).

Thread-aware Adaptive Prefetcher on Multicore Systems: Improving the Performance for Multithreaded Workloads

This article proposes a thread-aware self-tuning data prefetcher, which consists of three new components: 1) a self-tuning prefetcher that uses runtime feedback to dynamically adjust each threads data prefetching modes and arguments, 2) a filtering mechanism that informs the hardware about which prefetching requests can cause shared data invalidation and should be discarded, and 3) a limiter thread acceleration to estimate and accelerate the critical threads. On a set of multithreaded parallel benchmarks, our approach improves the performance of 64-core system by 13% and reduces the energy-delay product by 14% over a multi-mode prefetch baseline system.

Understanding and Mitigating Covert Channels Through Branch Predictors

Covert channels through shared processor resources provide secret communication between two malicious processes - the trojan and the spy. In this paper, we classify, analyze and compare covert channels through dynamic branch prediction units in modern processors. We compare contention-based channel, and the channel that is based on exploiting branch predictors residual state. We compare both channels in detail. We also propose a software-only mitigation technique that is based on randomizing the state of the predictor tables on context switches. We show that this protection can effectively close this type of covert channels with minimal impact on performance.

Power Efficient Hardware Transactional Memory: Dynamic Issue of Transactions

Transactional Memory (TM) is not just an academic interest any more as industry has started to adopt the idea in its commercial products. In this paper, we propose Dynamic Transaction Issue, a new scheme that can be easily implemented on top of existing HTM (Hardware TM) systems. Instead of wasting power in transaction aborts, Dynamic Transaction Issue puts a processor core into an idle state when there is a reasonable suspicion that the current transaction running on it will be aborted soon in the future.

Autotuning Runtime Specialization for Sparse Matrix-Vector Multiplication

Runtime specialization is used for optimizing programs based on partial information available only at run time. In this paper we apply auto-tuning on runtime specialization of Sparse Matrix-Vector Multiplication to predict a specialization method among several. In 91-96% of the predictions, either the best or the second best method is chosen. Predictions achieve average speedups that are very close to the speedups achievable when only the best methods are used. By using an efficient code generator and a reduced set of matrix features, we show the total runtime costs can be amortized to bring performance benefits for many real-world cases.

Boosting the Priority of Garbage: Scheduling Collection on Heterogeneous Multicore Processors

While hardware is evolving towards heterogeneous multicore architectures, modern software applications are increasingly written in managed languages. A key component of any managed runtime is the garbage collector. In this paper, we explore concurrent garbage collection's behavior, and how to schedule it on a heterogeneous system to optimize application performance. We develop a new, adaptive scheduling algorithm that responds to GC criticality signals from the managed runtime, giving more big core cycles to the concurrent collector when it is under pressure and in danger of suspending the application.

Bibliometrics

Publication Years 2004-2016
Publication Count 449
Citation Count 1392
Available for Download 449
Downloads (6 weeks) 3210
Downloads (12 Months) 26051
Downloads (cumulative) 175118
Average downloads per article 390
Average citations per article 3
First Name Last Name Award
Sarita Adve ACM Fellows (2010)
Iris Bahar ACM Distinguished Member (2012)
Laxmi N Bhuyan ACM Fellows (2000)
Douglas C Burger ACM Fellows (2010)
ACM Distinguished Member (2008)
ACM Senior Member (2006)
Martin Burtscher ACM Distinguished Member (2014)
ACM Senior Member (2006)
John B Carter ACM Senior Member (2015)
Jason Cong ACM Fellows (2008)
William J Dally ACM - IEEE CS Eckert-Mauchly Award (2010)
ACM Fellows (2002)
Jack Davidson ACM Fellows (2008)
Bronis R. De Supinski ACM Gordon Bell Prize (2006)
Michel Dubois ACM Fellows (2005)
Nikil D. Dutt ACM Fellows (2014)
ACM Distinguished Member (2007)
Joel Emer ACM - IEEE CS Eckert-Mauchly Award (2009)
ACM Fellows (2004)
Michael Franz ACM Fellows (2015)
ACM Distinguished Member (2006)
Guang Gao ACM Fellows (2007)
Phillip B Gibbons ACM Fellows (2006)
Phillip Gibbons ACM Fellows (2006)
Rajiv Gupta ACM Fellows (2009)
Mary W Hall ACM Distinguished Member (2010)
Tim Harris ACM Senior Member (2007)
Mark Hill ACM Fellows (2004)
Mark A Horowitz ACM Fellows (2003)
Mary Jane Irwin ACM-W Athena Lecturer Award (2010)
ACM Distinguished Service Award (2005)
ACM Fellows (1996)
Norman Jouppi ACM - IEEE CS Eckert-Mauchly Award (2015)
ACM Fellows (2006)
David R Kaeli ACM Distinguished Member (2014)
Stephen Keckler ACM Fellows (2011)
ACM Senior Member (2006)
ACM Grace Murray Hopper Award (2003)
Nectarios Koziris ACM Senior Member (2009)
Sharad Malik ACM Fellows (2014)
Margaret Martonosi ACM Fellows (2009)
Sally A McKee ACM Senior Member (2013)
Shubu Mukherjee ACM Fellows (2011)
ACM Distinguished Member (2009)
Walid Najjar ACM Distinguished Member (2015)
ACM Senior Member (2014)
Vijaykrishnan Narayanan ACM Fellows (2014)
David Padua ACM Fellows (2007)
Milos Prvulovic ACM Senior Member (2009)
Lawrence Rauchwerger ACM Distinguished Member (2014)
Vivek Sarkar ACM Fellows (2008)
ACM Distinguished Member (2006)
Robert Schreiber ACM Fellows (2012)
ACM Distinguished Member (2006)
Martin Schulz ACM Gordon Bell Prize (2006)
Kevin Skadron ACM Fellows (2015)
ACM Distinguished Member (2010)
ACM Senior Member (2007)
Mary Lou Soffa ACM - IEEE CS Ken Kennedy Award (2012)
ACM Fellows (1999)
Leonel Sousa ACM Distinguished Member (2015)
ACM Senior Member (2009)
J. Gregory Steffan ACM Senior Member (2009)
Per O Stenstrom ACM Fellows (2008)
Subhash Suri ACM Fellows (2010)
ACM Distinguished Member (2007)
Josep Torrellas ACM Fellows (2010)
Dean M Tullsen ACM Fellows (2011)
Mateo Valero ACM Distinguished Service Award (2012)
ACM - IEEE CS Eckert-Mauchly Award (2007)
ACM Fellows (2002)
Hans Vandierendonck ACM Senior Member (2013)
Stamatis Vassiliadis ACM Fellows (2004)
Richard W. Vuduc ACM Gordon Bell Prize (2010)
David Whalley ACM Distinguished Member (2009)
ACM Senior Member (2009)
Andreas Zeller ACM Fellows (2010)
Lixia Zhang ACM Fellows (2006)
Yuanyuan Zhou ACM Fellows (2013)
ACM Distinguished Member (2011)
Yuanyuan Zhou ACM Fellows (2013)
ACM Distinguished Member (2011)

First Name Last Name Paper Counts
Lieven Eeckhout 16
André Seznec 8
Rajiv Gupta 8
Yuan Xie 8
Bruce Childers 7
Kevin Skadron 7
Stijn Eyerman 7
Michael O'Boyle 6
Mateo Valero 6
David Gregg 6
Antonia Zhai 6
Margaret Martonosi 6
Norman Jouppi 6
Yan Solihin 5
Pen Yew 5
Haitham Akkary 5
Shlomo Weiss 5
Jun Yang 5
Weichung Hsu 5
Timothy Sherwood 5
Christian Wimmer 5
Laxmi Bhuyan 5
Jingling Xue 5
David Whalley 5
Antonio González 5
Onur Mutlu 5
Jaume Abella 5
Adrián Cristal 5
Henk Corporaal 4
Milos Prvulovic 4
Miquel Moretó 4
Osman Ünsal 4
Chengyong Wu 4
Christos Kozyrakis 4
Francisco Cazorla 4
Yu Du 4
Timothy Jones 4
David Brooks 4
Jack Davidson 4
Tor Aamodt 4
Mateo Valero 4
Daniel Jiménez 4
Oğuz Ergin 4
Louis Pouchet 4
Sarita Adve 4
Ran Ginosar 3
Shuangde Fang 3
Dmitry Ponomarev 3
Ponnuswamy Sadayappan 3
Walid Najjar 3
Ramón Doallo 3
Josep Torrellas 3
José Llabería 3
Antoniu Pop 3
Rami Melhem 3
Chandra Krintz 3
Youtao Zhang 3
Prashant Nair 3
Marcelo Cintra 3
Daniel Mossé 3
Avi Mendelson 3
Yiannakis Sazeides 3
Rubén Titos-Gil 3
José García 3
Mahmut Kandemir 3
Frank Mueller 3
Mary Soffa 3
James Tuck 3
Jue Wang 3
Michael Franz 3
Hanspeter Mössenböck 3
Prasad Kulkarni 3
Víctor Viñals 3
Xiangyu Dong 3
María Garzarán 3
Kim Hazelwood 3
David Tarjan 3
Miao Zhou 3
Pierre Michaud 3
Todd Mowry 3
Hans Vandierendonck 3
Francky Catthoor 3
Moinuddin Qureshi 3
Lizy John 3
Basilio Fraguela 3
Wengfai Wong 3
Paolo Ienne 3
Aamer Jaleel 3
Yunji Chen 3
Jose Renau 3
Michael Spear 3
Chialin Yang 3
Motohiro Kawahito 2
Sebastian Hack 2
Betul Buyukkurt 2
Rupesh Nasre 2
Gennady Pekhimenko 2
Zhibin Yu 2
Xiaofei Liao 2
Lishiuan Peh 2
Yuanjie Huang 2
José Martínez 2
Changhui Lin 2
Guyeon Wei 2
Jorge Albericio 2
Alex Jones 2
Olivier Temam 2
Jagannathan Ramanujam 2
Zheng Wang 2
Michael Smith 2
Stefan Brunthaler 2
Ron Gabor 2
Jin Lin 2
Dean Tullsen 2
Neil Vachharajani 2
Bjorn De Sutter 2
Yunheung Paek 2
Wim Heirman 2
Ben Juurlink 2
Shashidhar Mysore 2
Magnus Själander 2
Fabrice Rastello 2
Albert Cohen 2
Leonid Yavits 2
Sanyam Mehta 2
Angeliki Kritikakou 2
Philippe Clauss 2
Christophe Dubach 2
Olivier Temam 2
Stefanos Kaxiras 2
Alex Ramírez 2
Jason Hiser 2
Jianjun Li 2
David August 2
Simon Steely 2
Anurag Negi 2
Ramesh Karri 2
Nathanael Premillieu 2
Venmugil Elango 2
Wenjia Ruan 2
Scott Mahlke 2
Chuntao Jiang 2
Nael Abu-Ghazaleh 2
Bronis De Supinski 2
Gabriel Loh 2
Tinfook Ngai 2
Hwansoo Han 2
Min Feng 2
Xuejun Yang 2
William Hasenplaugh 2
Alberto Ros 2
Sven Verdoolaege 2
Morteza Kafshdooz 2
Alireza Ejlali 2
Uday Bondhugula 2
Quan Chen 2
Xiaobing Feng 2
John Kim 2
Cedric Nugteren 2
Maximilien Breughe 2
Michael O’boyle 2
Glenn Reinman 2
Grigori Fursin 2
Jacob Leverich 2
Karthik Sankaranarayanan 2
Frederic Chong 2
Komal Jothi 2
Yuanyuan Zhou 2
Abhishek Bhattacharjee 2
Manlap Li 2
Joel Emer 2
Banit Agrawal 2
Yong Li 2
Doug Simon 2
Yang Chen 2
Mary Irwin 2
Gautham Beeraka 2
Mauricio Álvarez-Mesa 2
Joseph Sharkey 2
Todd Austin 2
Edwin Bonilla 2
Marco Santambrogio 2
Kishore Pusukuri 2
Amit Golander 2
Diego Andrade 2
Ravishankar Iyer 2
Li Zhao 2
Yangchun Luo 2
Mageda Sharafeddine 2
Bart Coppens 2
Pablo Ibáñez 2
Michela Becchi 2
Arun Kanuparthi 2
Rajkishore Barik 2
Yosi Asher 2
Yuanshin Hwang 2
Jason Loew 2
Costas Goutis 2
Per Larsen 2
David Roberts 2
Chuanjun Zhang 2
Oliverio Santana 2
Chenggang Wu 2
Jason Cong 2
Trevor Carlson 2
Jungho Ahn 2
Ghassan Shobaki 2
Naveen Muralimanohar 2
Amir Morad 2
Xiangyu Dong 2
Vasilios Kelefouras 2
Qixiao Liu 2
Roy Ju 2
Guru Venkataramani 2
Ram Rangan 2
Ibrahim Hur 2
Erven Rohou 2
Karthikeyan Sankaralingam 2
Yu Chen 1
Michael Haupt 1
Dave Herman 1
Jianhua Sun 1
Jisheng Zhao 1
Germán Rodríguez 1
Zheng Cui 1
Christos Strydis 1
Samira Khan 1
Alan Cox 1
Scott Rixner 1
Pen Yew 1
Revital Eres 1
Atanas Rountev 1
Tryggve Fossum 1
Shuai Mu 1
Biswabandan Panda 1
Chao Zhang 1
Yuntan Fang 1
Aravind Acharya 1
Vinay Vasista 1
Wenhao Jia 1
Kelly Shaw 1
Thomas Würthinger 1
Eric Hein 1
Jack Sampson 1
Vinay Gangadhar 1
Ali Bakhoda 1
Hiroshi Inoue 1
Toshio Nakatani 1
Avinash Malik 1
Eran Shifer 1
Jie Zhou 1
Jinbo Xu 1
Jan Martinsen 1
Anders Isberg 1
Radu Teodorescu 1
Ramaswamy Govindarajan 1
Martin Kong 1
Enrique Vallejo 1
Wei Tong 1
Helge Bahmann 1
Nico Reissmann 1
Dominik Grewe 1
David Ham 1
Laura Carrington 1
Ali Jannesari 1
Mihail Popov 1
William Jalby 1
Naghmeh Karimi 1
Beayna Grigorian 1
Xueyang Wang 1
Ozgur Sinanoglu 1
Rongguey Chang 1
Nitya Ranganathan 1
Doug Burger 1
Charles Moore 1
Yatish Patel 1
Dean Copsey 1
Narayanan Vijaykrishnan 1
Lei Liu 1
Mingyu Chen 1
Zehan Cui 1
Huahan Zhang 1
Hamid Sarbazi-Azad 1
Christian Häubl 1
Preeti Panda 1
Georgios Tournavitis 1
Y Srikant 1
William Dally 1
Ehsan Ardestani 1
Zizhong Chen 1
Heikki Kultala 1
Hui Feng 1
Jaydeep Marathe 1
Barry Rountree 1
Edmund Lai 1
Thomas Puzak 1
Allan Hartstein 1
Alvin Lebeck 1
Hungwei Tseng 1
Yu Bai 1
Philo Juang 1
Karin Strauss 1
George Athanasiou 1
Lixin Zhang 1
Qin Zhao 1
Ioana Cutcutache 1
Kornilios Kourtis 1
Georgios Goumas 1
Sangyeun Cho 1
Ahmad Samih 1
Arun Raghavan 1
José GonzáLez 1
Jialin Dou 1
Fred Bower 1
Sandeep Gupta 1
Thomas Wild 1
Stephen Plaza 1
Jason Blome 1
Vassos Soteriou 1
Yan Luo 1
Ernst Gran 1
Bartosz Bogdański 1
John Demme 1
Simha Sethumadhavan 1
Jason McCandless 1
Jinquan Dai 1
Yunhong Jiang 1
Pritpal Ahuja 1
Niket Choudhary 1
Evgeny Bolotin 1
Petar Radojković 1
Oded Maler 1
Rainer Buchty 1
Javier Lira 1
Kelly Livingston 1
John Cavazos 1
Ting Cao 1
Marek Chrobak 1
Joshua Fryman 1
Paolo Prinetto 1
Venkata Tavva 1
Rakesh Kumar 1
Thomas Conte 1
Martin Schulz 1
Richard Strong 1
Nisheeth Shrivastava 1
Ivan Sham 1
Ioannis Doudalis 1
Brian Fields 1
Kristof Du Bois 1
Albert Cohen 1
Jimmy Cleary 1
Mark Purcell 1
Pablo Abad 1
Jinseong Jeon 1
Huimin Cui 1
Suresh Purini 1
Fabien Coelho 1
Chun Chen 1
Servesh Muralidharan 1
Albert Cohen 1
Murray Cole 1
Mick Jordan 1
Ehsan Totoni 1
Andrei Hagiescu 1
Ioannis Sourdis 1
Marcel Zalmanovici 1
Zhao Zhang 1
Daniel Jiménez-González 1
Sergei Dyshel 1
Bin Ren 1
Deyu Shen 1
Bernhard Urban 1
Lukas Stadler 1
Wei Shu 1
Naifeng Jing 1
Minyou Wu 1
Xiaoyao Liang 1
Ivan Stalev 1
Pradip Valathol 1
Jaikrishnan Menon 1
Gabriel Southern 1
Yongwen Wang 1
Zheng Wang 1
Håkan Grahn 1
Xing Zhou 1
Michael Kozuch 1
Jan Meyer 1
Gheorghe Bercea 1
Adam Jundt 1
Benedict Gaster 1
Jason Villarreal 1
Michel Boyer 1
Fang Liu 1
Wei Huang 1
Isaac Chuang 1
John Kubiatowicz 1
Mark Oskin 1
Mark Whitney 1
Vijay Degalahal 1
Yongbing Huang 1
Yungang Bao 1
Irshad Pananilath 1
Bharath Swamy 1
Rajesh Gupta 1
Robin Paul 1
Hsiangyun Cheng 1
Najm Rmaileh 1
Yazhuo Dong 1
Hongjian Li 1
Libo Huang 1
Jungho Ahn 1
Simon Moll 1
George Matheou 1
Rakesh Komuravelli 1
Hongyi Xin 1
Ely Levy 1
Zhenman Fang 1
Binyu Zang 1
Chen Li 1
Morteza Hoseinzadeh 1
Neeraj Goel 1
Bradley Thwaites 1
Rajshekar Kalayappan 1
Shuaiwenleon Song 1
Raul Vidal 1
Pedro Tomás 1
Tapio Elomaa 1
Sepehr Assadi 1
Jiang Zheng 1
Madhavi Valluri 1
Sriraman Tallam 1
Ritu Gupta 1
José Duato 1
Yan Meng 1
John Carter 1
Li Zhang 1
Junwhan Ahn 1
Stephen Hines 1
Daniel Williams 1
Chingwei Yeh 1
Changching Yeh 1
Jieyi Long 1
Meeta Gupta 1
Grigorios Magklis 1
Pedro Chaparro 1
Choonki Jang 1
Bernhard Egger 1
Rachid Seghir 1
Vincent Loechner 1
Jingfei Kong 1
Huiyang Zhou 1
Georgios Varsamopoulos 1
Chen Cher 1
Gilberto Contreras 1
Kypros Constantinides 1
Eric Debes 1
Yenkuang Chen 1
Jongeun Lee 1
Benoít Dupont De Dinechin 1
Yuliu Chen 1
Gerard Smit 1
Stijn Polfliet 1
Avinoam Kolodny 1
Alejandro Rico 1
Fabio Luporini 1
Ana Varbanescu 1
Florian Rathgeber 1
Roy Campbell 1
Zia Huda 1
Chiehting Huang 1
Jinho Suh 1
Vineeth Mekkat 1
Ahmad Zmily 1
Tzongyen Lin 1
Stephen Keckler 1
Yuan Ruan 1
Wei Zhang 1
Thomas Carle 1
Chiachen Chou 1
Mohammad Arjomand 1
Shuming Chen 1
Carlo Sicignano 1
Smruti Sarangi 1
Seonwook Kim 1
Li Tan 1
Dimitrios Chasapis 1
Jesús Labarta 1
Francisco Gaspar 1
Dingyong Hong 1
Tomi Aijo 1
Russell Tessier 1
Dee Weikle 1
Xiaodong Li 1
Gerd Ascheid 1
Pin Zhou 1
Polychronis Xekalakis 1
Nikolas Ioannou 1
Chienhao Lee 1
Ruth Bahar 1
Douglas Clark 1
Josep Larriba-Pey 1
Bo Zhao 1
Kyuseung Han 1
Douglas Jones 1
Christopher Hughes 1
Yujung Cheng 1
Ying Zhang 1
Xudong Fang 1
Wei Lin 1
Qiong Cai 1
Farrukh Hijaz 1
Pradeep Ramachandran 1
Siva Hari 1
Piero Olivo 1
Saurabh Sharma 1
Karan Singh 1
Sally McKee 1
Wei Zhang 1
Rong Chen 1
Stamatis Vassiliadis 1
J Steffan 1
Daofu Liu 1
Chengkok Koh 1
Yiran Chen 1
Hai Li 1
Brian Rogers 1
Jonas Maebe 1
Owen Callanan 1
Jacqueline Chame 1
Ozcan Ozturk 1
Greg Brown 1
John Wernsing 1
Kyoungwoo Lee 1
Bita Mazloom 1
Walid Ghandour 1
Zhijia Zhao 1
Narasimha Reddy 1
Paul Gratz 1
Vivek Sarkar 1
Hai Jin 1
Lieven Eeckhout 1
Yaojun Zhang 1
Dimitrios Siskos 1
Yanan Cao 1
Per Larsson-Edefors 1
Dimitrios Nikolopoulos 1
Carl Beckmann 1
Yi Xu 1
Wonsub Kim 1
Abbas BanaiyanMofrad 1
Kevin Lim 1
Paolo Faraboschi 1
Zhengwei Qi 1
Víctor Yúfera 1
Yungcheng Ma 1
Mazen Saghir 1
Daniel Sorin 1
Soojung Ryu 1
Fuxin Zhang 1
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ACM Transactions on Architecture and Code Optimization (TACO)
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