Ruud A. Haring

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13ranked-venue papers

1as first author

0since 2021 · last 2014

—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 12 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems

4 papers

Electronic design automation · 59% High-performance computing · 24% Integrated circuit design · 17%

Topics — the 8 heaviest of 8, each with the papers that count most for it

Topic	Weight	Papers	Last	Evidence papers
Electronic design automation › design methodology design flows and tools	0.1	1	2010	What will make your next design experience a much better one? · DAC 2010
Electronic design automation › logic synthesis circuit optimization	0.0	2	2000	Noise considerations in circuit optimization · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2000 JiffyTune: circuit optimization using time-domain sensitivities · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 1998
High-performance computing › supercomputing bluegene/l	0.0	1	2002	An overview of the BlueGene/L Supercomputer · SC 2002
High-performance computing supercomputing	0.0	1	2002	An overview of the BlueGene/L Supercomputer · SC 2002
Integrated circuit design system-on-chip	0.0	1	2002	An overview of the BlueGene/L Supercomputer · SC 2002
Electronic design automation circuit simulation	0.0	1	1998	JiffyTune: circuit optimization using time-domain sensitivities · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 1998
Integrated circuit design digital circuit design	0.0	1	2000	Noise considerations in circuit optimization · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2000
Integrated circuit design › digital circuit design dynamic logic	0.0	1	2000	Noise considerations in circuit optimization · IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 2000

Methods — techniques the papers use, named apart from their topics

system architecture design · 0.0performance scaling studies · 0.0semi-infinite constraint transformation · 0.0augmented lagrangian optimization · 0.0adjoint sensitivity analysis · 0.0nonlinear optimization · 0.0direct method · 0.0adjoint method · 0.0adjoint lagrangian method · 0.0

Year	Publication	Venue	Position
2014	Soft Error Resiliency Characterization on IBM BlueGene/Q Processor abstract Soft Error Resiliency (SER) is a major concern for Petascale high performance computing (HPC) systems. In designing Blue Gene/Q (BG/Q) [8], many mechanisms were deployed to target SER including extensive use of Silicon-On-Insulator (SOI), radiation-hardened latches [7,13], detection and correction in on-chip arrays, and very low radiation packaging materials. On the other hand, it is well known that application behavior has major impacts on the masking (or “derating” factor) in system SER calculations. The principal goal of this project is to understand the interaction between BG/Q hardware and high-performance applications when it comes to SER by performing and evaluating a chip irradiation experiment. Chen-Yong Cher, K. Paul Muller, Ruud A. Haring, David L. Satterfield, Thomas E. Musta, Thomas Gooding, Kristan D. Davis, Marc Boris Dombrowa, Gerard V. Kopcsay, Robert M. Senger, Yutaka Sugawara, Krishnan Sugavanam	ASP-DAC	3
2014	Soft error resiliency characterization and improvement on IBM BlueGene/Q processor using accelerated proton irradiation abstract Fault injection through accelerated irradiation is an effective way to evaluate the overall soft error resiliency of microprocessors. In this work, we report on irradiation experiments on a Blue Gene/Q (BG/Q) compute processor chip running selected applications. Blue Gene/Q is the third generation of IBM's massively parallel, energy efficient Blue Gene series of supercomputers. In the experiments, we found 69 code fails. Out of these, 26 code fails are relevant for the calculation of the mean-time-between-failures (MTBF) for a 20 PetaFLOP, 96 rack system running a comparable workload mix. The expected MTBF for check-stops due to cosmic radiation and alpha particles from chip packaging materials is calculated to be 51 days for sea-level at New York City running the application mix studied. If the most vulnerable application is run exclusively, the projected MTBF is 35 days. These are outstanding results for a machine of this magnitude. The beaming experiment and projected MTBF validate the necessity to include autonomous hardware detection and recovery at the cost of design effort, silicon area and power. Chen-Yong Cher, K. Paul Muller, Ruud A. Haring, David L. Satterfield, Thomas E. Musta, Thomas Gooding, Kristan D. Davis, Marc Boris Dombrowa, Gerard V. Kopcsay, Robert M. Senger, Yutaka Sugawara, Krishnan Sugavanam	ITC	3
2011	The Blue Gene/Q Compute chip Ruud A. Haring	Hot Chips Symposium	1
2010	What will make your next design experience a much better one? abstract Top designers and design engineering managers will present this wish list to make their next (or even current) design a much better design experience. They will not focus on pie-in-the-sky research topics, but describe down-to-earth challenges that designers face in getting their designs out the door. The panelists will use their data and experiences to substantiate their wish lists and address the main question: What needs to change in the design flows and design tools to improve Time-to-Market (TTM) and design quality? Thomas Harms, Juan-Antonio Caraballo, Reynold D'Sa, Ruud A. Haring, Derek Urbaniak, Guntram Wolski, James You	DAC	4
2004	The eDRAM based L3-Cache of the BlueGene/L Supercomputer Processor Node abstract BlueGene/L is a supercomputer consisting of 64K dual-processor system-on-a-chip compute nodes, capable of delivering an arithmetic peak performance of 5.6Gflops per node. To match the memory speed to the high compute performance, the system implements an aggressive three-level on-chip cache hierarchy for each node. The implemented hierarchy offers high bandwidth and integrated prefetching on cache hierarchy levels 2 and 3 to reduce memory access time. The integrated L3-cache stores a total of 4MB of data, using multibank embedded DRAM. The 1024 bit wide data port of the embedded DRAM provides 22.4GB/s bandwidth to serve the speculative prefetching demands of the two processor cores and the Gigabit Ethernet DMA engine. Martin Ohmacht, Dirk Hoenicke, Ruud A. Haring, Alan Gara	SBAC-PAD	3
2002	Blue Gene/L, a System-On-A-Chip abstract Summary form only given. Large powerful networks coupled to state-of-the-art processors have traditionally dominated supercomputing. As technology advances, this approach is likely to be challenged by a more cost-effective System-On-A-Chip approach, with higher levels of system integration. The scalability of applications to architectures with tens to hundreds of thousands of processors is critical to the success of this approach. Significant progress has been made in mapping numerous compute-intensive applications, many of them grand challenges, to parallel architectures. Applications hoping to efficiently execute on future supercomputers of any architecture must be coded in a manner consistent with an enormous degree of parallelism. The BG/L program is developing a peak nominal 180 TFLOPS (360 TFLOPS for some applications) supercomputer to serve a broad range of science applications. BG/L generalizes QCDOC, the first System-On-A-Chip supercomputer that is expected in 2003. BG/L consists of 65,536 nodes, and contains five integrated networks: a 3D torus, a combining tree, a Gb Ethernet network, barrier/global interrupt network and JTAG. George S. Almási, Daniel K. Beece, Ralph Bellofatto, Gyan Bhanot, Randy Bickford, Matthias A. Blumrich, Arthur A. Bright, José R. Brunheroto, Calin Cascaval, José G. Castaños, Luis Ceze, Paul Coteus, Siddhartha Chatterjee, Dong Chen 0005, George L.-T. Chiu, Thomas M. Cipolla, Paul Crumley, Alina Deutsch, Marc Boris Dombrowa, Wilm E. Donath, Maria Eleftheriou, Blake G. Fitch, Joseph Gagliano, Alan Gara, Robert S. Germain, Mark Giampapa, Manish Gupta 0002, Fred G. Gustavson, Shawn Hall, Ruud A. Haring, David F. Heidel, Philip Heidelberger, Lorraine M. Herger, Dirk Hoenicke, T. Jamal-Eddine, Gerard V. Kopcsay, Alphonso P. Lanzetta, Derek Lieber, M. Lu, Mark P. Mendell, Lawrence S. Mok, José E. Moreira, Ben J. Nathanson, Matthew Newton, Martin Ohmacht, Rick A. Rand, Richard D. Regan, Ramendra K. Sahoo, Alda Sanomiya, Eugen Schenfeld, Sarabjeet Singh, Peilin Song, Burkhard D. Steinmacher-Burow, Karin Strauss, Richard A. Swetz, Todd Takken, R. Brett Tremaine, Mickey Tsao, Pavlos Vranas, T. J. Christopher Ward, Michael E. Wazlowski, J. Brown, Thomas A. Liebsch, A. Schram, G. Ulsh	CLUSTER	30
2002	An overview of the BlueGene/L Supercomputer abstract This paper gives an overview of the BlueGene/L Supercomputer. This is a jointly funded research partnership between IBM and the Lawrence Livermore National Laboratory as part of the United States Department of Energy ASCI Advanced Architecture Research Program. Application performance and scaling studies have recently been initiated with partners at a number of academic and government institutions,including the San Diego Supercomputer Center and the California Institute of Technology. This massively parallel system of 65,536 nodes is based on a new architecture that exploits system-on-a-chip technology to deliver target peak processing power of 360 teraFLOPS (trillion floating-point operations per second). The machine is scheduled to be operational in the 2004-2005 time frame, at price/performance and power consumption/performance targets unobtainable with conventional architectures. Narasimha R. Adiga, Gheorghe Almási 0001, George S. Almási, Yariv Aridor, Rajkishore Barik, Daniel K. Beece, Ralph Bellofatto, Gyan Bhanot, Randy Bickford, Matthias A. Blumrich, Arthur A. Bright, José R. Brunheroto, Calin Cascaval, José G. Castaños, Waiman Chan, Luis Ceze, Paul Coteus, Siddhartha Chatterjee, Dong Chen 0005, George L.-T. Chiu, Thomas M. Cipolla, Paul Crumley, K. M. Desai, Alina Deutsch, Tamar Domany, Marc Boris Dombrowa, Wilm E. Donath, Maria Eleftheriou, C. Christopher Erway, J. Esch, Blake G. Fitch, Joseph Gagliano, Alan Gara, Rahul Garg 0001, Robert S. Germain, Mark Giampapa, Balaji Gopalsamy, John A. Gunnels, Manish Gupta 0002, Fred G. Gustavson, Shawn Hall, Ruud A. Haring, David F. Heidel, Philip Heidelberger, Lorraine M. Herger, Dirk Hoenicke, R. D. Jackson, T. Jamal-Eddine, Gerard V. Kopcsay, Elie Krevat, Manish P. Kurhekar, Alphonso P. Lanzetta, Derek Lieber, L. K. Liu, M. Lu, Mark P. Mendell, A. Misra, Yosef Moatti, Lawrence S. Mok, José E. Moreira, Ben J. Nathanson, Matthew Newton, Martin Ohmacht, Adam J. Oliner, Vinayaka Pandit, R. B. Pudota, Rick A. Rand, Richard D. Regan, Bradley Rubin, Albert E. Ruehli, Silvius Vasile Rus, Ramendra K. Sahoo, Alda Sanomiya, Eugen Schenfeld, M. Sharma, Edi Shmueli, Sarabjeet Singh, Peilin Song, Vijay Srinivasan, Burkhard D. Steinmacher-Burow, Karin Strauss, Christopher W. Surovic, Richard A. Swetz, Todd Takken, R. Brett Tremaine, Mickey Tsao, Arun R. Umamaheshwaran, P. Verma, Pavlos Vranas, T. J. Christopher Ward, Michael E. Wazlowski, W. Barrett, C. Engel, B. Drehmel, B. Hilgart, D. Hill, F. Kasemkhani, David J. Krolak, Chun-Tao Li 0001, Thomas A. Liebsch, James A. Marcella, A. Muff, A. Okomo, M. Rouse, A. Schram, M. Tubbs, G. Ulsh, Charles D. Wait, J. Wittrup, Myung Bae, Kenneth A. Dockser, Lynn Kissel, Mark K. Seager, Jeffrey S. Vetter, K. Yates	SC	42
2000	Noise considerations in circuit optimization abstract Noise can cause digital circuits to switch incorrectly, producing spurious results. It can also have adverse power, timing and reliability effects. Dynamic logic is particularly susceptible to charge-sharing and coupling noise. Thus, the design and optimization of a circuit should take noise considerations into account. Such considerations are typically stated as semi-infinite constraints in the time-domain. Semi-infinite problems are generally harder to solve than standard nonlinear optimization problems. Moreover, the number of noise constraints can potentially be very large. This paper describes a novel and practical method for incorporating realistic noise considerations during automatic circuit optimization by representing semi-infinite constraints as ordinary equality constraints involving time integrals. Using an augmented Lagrangian optimization merit function, the adjoint method is applied to compute all the gradients required for optimization in a single adjoint analysis, no matter how many noise measurements are considered and irrespective of the dimensionality of the problem. Thus, for the first time, a method is described to practically accommodate a large number of noise considerations during circuit optimization. The technique has been applied to optimization using time-domain simulation, but could be applied in the future to optimization on a static-timing basis. Numerical results are presented. Chandramouli Visweswariah, Ruud A. Haring, Andrew Conn 0001	IEEE Trans. Comput. Aided Des. Integr. Circuits Syst.	2
1998	Noise considerations in circuit optimization abstract Noise can cause digital circuits to switch incorrectly and thus produce spurious results. Noise can also have adverse power, timing and reliability effects. Dynamic logic is particularly susceptible to charge-sharing and coupling noise. Thus the design and optimization of a circuit should take noise considerations into account. Such considerations are typically stated as semi-infinite constraints. In addition, the number of signals to be checked and the number of sub-intervals of time during which the checking must be performed can potentially be very large. Thus, the practical incorporation of noise constraints during circuit optimization is a hitherto unsolved problem. This paper Andrew Conn 0001, Ruud A. Haring, Chandramouli Visweswariah	ICCAD	2
1998	Designing a Testable System on a Chip abstract A "system on a chip" is described, which integrates 16 Mbits of DRAM, digital logic, SRAM, three PLLs, and a triple video digital-to-analog converter in a 0.5 micron CMOS DRAM process. Application specific integrated circuit (ASIC) techniques are employed, using multiple DRAM macros with built-in self test (BIST), full level-sensitive scan design (LSSD) logic, and externally accessible analog circuitry. Issues regarding functional debugging, DRAM macro isolation and low cost manufacturing test using only a logic tester are described. Stephen V. Kosonocky, Arthur A. Bright, Kevin W. Warren, Ruud A. Haring, Steve Klepner, Sameh W. Asaad, S. Basavaiah, Bob Havreluk, David F. Heidel, Michael Immediato, Keith A. Jenkins, Rajiv V. Joshi, Benjamin D. Parker, T. V. Rajeevakumar, Kevin Stawiasz	VTS	4
1998	JiffyTune: circuit optimization using time-domain sensitivities abstract Automating the transistor and wire-sizing process is an important step toward being able to rapidly design high-performance, custom circuits. This paper presents a circuit optimization tool that automates the tuning task by means of state-of-the-art nonlinear optimization. It makes use of a fast circuit simulator and a general-purpose nonlinear optimization package. It includes minimax and power optimization, simultaneous transistor and wire tuning, general choices of objective functions and constraints, and recovery from nonworking circuits. In addition, the tool makes use of designer-friendly interfaces that automate the specification of the optimization task, the running of the optimizer, and the back-annotation of the results of optimization onto the circuit schematic. Particularly for large circuits, gradient computation is usually the bottleneck in the optimization procedure. In addition to traditional adjoint and direct methods, we use a technique called the adjoint Lagrangian method, which computes all the gradients necessary for one iteration of optimization in a single adjoint analysis. This paper describes the algorithms and the environment in which they are used and presents extensive circuit optimization results. A circuit with 6900 transistors, 4128 tunable transistors, and 60 independent parameters was optimized in about 108 min of CPU time on an IBM RISC/System 6000, model 590. Andrew Conn 0001, Paula K. Coulman, Ruud A. Haring, Gregory L. Morrill, Chandramouli Visweswariah, Chai Wah Wu	IEEE Trans. Comput. Aided Des. Integr. Circuits Syst.	3
1997	Circuit optimization via adjoint Lagrangians abstract The circuit tuning problem is best approached by means of gradient-based nonlinear optimization algorithms. For large circuits, gradient computation can be the bottleneck in the optimization procedure. Traditionally, when the number of measurements is large relative to the number of tunable parameters, the direct method is used to repeatedly solve the associated sensitivity circuit to obtain all the necessary gradients. Likewise, when the parameters outnumber the measurements, the adjoint method is employed to solve the adjoint circuit repeatedly for each measurement to compute the sensitivities. In this paper we propose the adjoint Lagrangian method, which computes all the gradients necessary for augmented-Lagrangian-based optimization in a single adjoint analysis. After the nominal simulation of the circuit has been carried out, the gradients of the merit function are expressed as the gradients of a weighted sum of circuit measurements. The weights are dependent on the nominal solution and on optimizer quantities such as Lagrange multipliers. By suitably choosing the excitations of the adjoint circuit, the gradients of the merit function are computed via a single adjoint analysis, irrespective of the number of measurements and the number of parameters of the optimization. This procedure requires close integration between the nonlinear optimization software and the circuit simulation program. Andrew Conn 0001, Ruud A. Haring, Chandramouli Visweswariah, Chai Wah Wu	ICCAD	2
1996	Optimization of custom MOS circuits by transistor sizing abstract Optimization of a circuit by transistor sizing is often a slow, tedious and iterative manual process which relies on designer intuition. Circuit simulation is carried out in the inner loop of this tuning procedure. Automating the transistor sizing process is an important step towards being able to rapidly design high-performance, custom circuits. JiffyTune is a new circuit optimization tool that automates the tuning task. Delay, rise/fall time, area and power targets are accommodated. Each (weighted) target can be either a constraint or an objective function. Minimax optimization is supported. Transistors can be ratioed and similar structures grouped to ensure regular layouts. Bounds on transistor widths are supported. JiffyTune uses LANCELOT, a large-scale nonlinear optimization package with an augmented Lagrangian formulation. Simple bounds are handled explicitly and trust region methods are applied to minimize a composite objective function. In the inner loop of the optimization, the fast circuit simulator SPECS is used to evaluate the circuit. SPECS is unique in its ability to efficiently provide time-domain sensitivities, thereby enabling gradient-based optimization. Both the adjoint and direct methods of sensitivity computation have been implemented in SPECS. To assist the user, interfaces in the Cadence and SLED design systems have been constructed. Andrew Conn 0001, Paula K. Coulman, Ruud A. Haring, Gregory L. Morrill, Chandramouli Visweswariah	ICCAD	3