D. E. Shaw Research

About: D. E. Shaw Research is a company organization based out in New York, New York, United States. It is known for research contribution in the topics: Massively parallel & G protein-coupled receptor. The organization has 233 authors who have published 273 publications receiving 38359 citations.

BlueGene/L applications: Parallelism On a Massive Scale

Abstract: BlueGene/L (BG/L), developed through a partnership between IBM and Lawrence Livermore National Laboratory (LLNL), is currently the world's largest system both in terms of scale, with 131,072 processors, and absolute performance, with a peak rate of 367 Tflop/s. BG/L has led the last four Top500 lists with a Linpack rate of 280.6 Tflop/s for the full machine installed at LLNL and is expected to remain the fastest computer in the next few editions. However, the real value of a machine such as BG/L derives from the scientific breakthroughs that real applications can produce by successfully using its unprecedented scale and computational power. In this paper, we describe our experiences with eight large scale applications on BG/ L from several application domains, ranging from molecular dynamics to dislocation dynamics and turbulence simulations to searches in semantic graphs. We also discuss the challenges we faced when scaling these codes and present several successful optimization techniques. All applications show excellent scaling behavior, even at very large processor counts, with one code even achieving a sustained performance of more than 100 Tflop/s, clearly demonstrating the real success of the BG/L design.

Early formal verification of conditional coverage points to identify intrinsically hard-to-verify logic

Abstract: Design verification of complex digital circuits typically starts only after the register-transfer level (RTL) description is complete. This frequently makes verification more difficult than necessary because logic that is intrinsically hard to verify, such as memories, counters and deep first-in, first-out (FIFO) structures, becomes immutable in the design. This paper proposes a new approach that exploits formal verification of conditional coverage points with the goal of early identification of hard-to-verify logic. We use the difficulty of formal verification problems as an early estimator of the verification complexity of a design. While traditional verification methods consider conditional coverage only in the design verification phase, we describe an approach that uses conditional coverage at a much earlier stage---the design phase, during which changes to the RTL code are still possible. The method is illustrated using real examples from the verification of an ASIC designed for a specialized supercomputer.

Structural basis for ALK2/BMPR2 receptor complex signaling through kinase domain oligomerization.

Abstract: Upon ligand binding, bone morphogenetic protein (BMP) receptors form active tetrameric complexes, comprised of two type I and two type II receptors, which then transmit signals to SMAD proteins. The link between receptor tetramerization and the mechanism of kinase activation, however, has not been elucidated. Here, using hydrogen deuterium exchange mass spectrometry (HDX-MS), small angle X-ray scattering (SAXS) and molecular dynamics (MD) simulations, combined with analysis of SMAD signaling, we show that the kinase domain of the type I receptor ALK2 and type II receptor BMPR2 form a heterodimeric complex via their C-terminal lobes. Formation of this dimer is essential for ligand-induced receptor signaling and is targeted by mutations in BMPR2 in patients with pulmonary arterial hypertension (PAH). We further show that the type I/type II kinase domain heterodimer serves as the scaffold for assembly of the active tetrameric receptor complexes to enable phosphorylation of the GS domain and activation of SMADs. Bone morphogenetic protein (BMP) receptors are single pass transmembrane serine/threonine kinases that form tetrameric complexes comprised of two type I and two type II BMP receptors. Here the authors characterize a structure of an active type I/type II kinase tetramer providing insight into molecular mechanism driving ligand-induced signaling.

Competing with automata-based expert sequences

Abstract: We consider a general framework of online learning with expert advice where regret is defined with respect to sequences of experts accepted by a weighted automaton. Our framework covers several problems previously studied, including competing against k-shifting experts. We give a series of algorithms for this problem, including an automata-based algorithm extending weighted-majority and more efficient algorithms based on the notion of failure transitions. We further present efficient algorithms based on an approximation of the competitor automaton, in particular n-gram models obtained by minimizing the∞-Rényi divergence, and present an extensive study of the approximation properties of such models. Finally, we also extend our algorithms and results to the framework of sleeping experts.

Efficient Transient Analysis of Power Delivery Network With Clock/Power Gating by Sparse Approximation

Abstract: Transient analysis of large-scale power delivery network (PDN) is a critical task to ensure the functional correctness and desired performance of today’s integrated circuits (ICs), especially if significant transient noises are induced by clock and/or power gating due to the utilization of extensive power management. In this paper, we propose an efficient algorithm for PDN transient analysis based on sparse approximation. The key idea is to exploit the fact that the transient response caused by clock/power gating is often localized and the voltages at many other “inactive” nodes are almost unchanged, thereby rendering a unique sparse structure. By taking advantage of the underlying sparsity of the solution structure, a modified conjugate gradient algorithm is developed and tuned to efficiently solve the PDN analysis problem with low computational cost. Our numerical experiments based on standard benchmarks demonstrate that the proposed transient analysis with sparse approximation offers up to $2.2\times $ runtime speedup over other traditional methods, while simultaneously achieving similar accuracy.