Advancing nanoelectronic device modeling through peta-scale computing and deployment on nanoHUB

Recent improvements to existing HPC codes NEMO 3-D and OMEN, combined with access to peta-scale computing resources, have enabled realistic device engineering simulations that were previously infeasible. NEMO 3-D can now simulate 1 billion atom systems, and, using 3D spatial decomposition, scale to 32768 cores. Simulation time for the band structure of an experimental P doped Si quantum computing device fell from 40 minutes to I minute. OMEN can perform fully quantum mechanical transport calculations for real-word UTB FETs on 147,456 cores in roughly 5 minutes. Both of these tools power simulation engines on the nanoHUB, giving the community access to previously unavailable research capabilities

Towards advanced symbolic analysis for optimizing compilers

Symbolic analysis is an enabling technique that improves the effectiveness of compiler optimizations, by detecting numeric properties of program variables or relationships between the variables, and by exposing such properties to compiler optimizations. Expressing the abstract values of program variables in terms of value ranges and analyzing expressions using the ranges are one of the most advanced symbolic analysis techniques to date. Previous work on this symbolic analysis has not fully explore its possibility in terms of coverage and utilization. This thesis aims at investigating possible advances in this symbolic analysis by providing two techniques, interprocedural symbolic range propagation and array value propagation. The two techniques were developed based on the symbolic analysis engine which utilizes the value ranges of variables. Interprocedural symbolic range propagation enables whole-program analysis for propagating scalar value ranges, and array value propagation uncovers value properties of array variables. The evaluation result shows that the two techniques increase the effectiveness of automatic parallelization and provide better compile-time knowledge so that the compiler improve the code quality using the knowledge

Interprocedural Symbolic Range Propagation for Optimizing Compilers ⋆

Abstract. We have designed and implemented an interprocedural algorithm to analyze symbolic value ranges that can be assumed by variables at any given point in a program. Our algorithm contrasts with related work on interprocedural value range analysis in that it extends the ability to handle symbolic range expressions. It builds on our previous work of intraprocedural symbolic range analysis. We have evaluated our algorithm using 11 Perfect Benchmarks and 10 SPEC floating-point benchmarks of the CPU 95 and CPU 2000 suites. We have measured the ability to perform test elision, dead code elimination, and detect data dependences. We have also evaluated the algorithm’s ability to help detect zero-trip loops for induction variable substitution and subscript ranges for array reductions.

Interprocedural Symbolic Range Propagation for Optimizing Compilers ⋆

Abstract. We have designed and implemented an interprocedural algorithm to analyze symbolic value ranges that can be assumed by variables at any given point in a program. Our algorithm contrasts with related work on interprocedural value range analysis in that it extends the ability to handle symbolic range expressions. It builds on our previous work of intraprocedural symbolic range analysis. We have evaluated our algorithm using 11 Perfect Benchmarks and 10 SPEC floating-point benchmarks of the CPU 95 and CPU 2000 suites. We have measured the ability to perform test elision, dead code elimination, and detect data dependences. We have also evaluated the algorithm’s ability to help detect zero-trip loops for induction variable substitution and subscript ranges for array reductions.

Performance Analysis of Symbolic Analysis Techniques for Parallelizing Compilers ⋆

Abstract. Understanding symbolic expressions is an important capability of advanced program analysis techniques. Many current compiler techniques assume that coefficients of program expressions, such as array subscripts and loop bounds, are integer constants. Advanced symbolic handling capabilities could make these techniques amenable to real application programs. Symbolic analysis is also likely to play an important role in supporting higher–level programming languages and optimizations. For example, entire algorithms may be recognized and replaced by better variants. In pursuit of this goal, we have measured the degree to which symbolic analysis techniques affect the behavior of current parallelizing compilers. We have chosen the Polaris parallelizing compiler and studied the techniques such as range analysis – which is the core symbolic analysis in the compiler – expression propagation, and symbolic expression manipulation. To measure the effect of a technique, we disabled it individually, and compared the performance of the resulting program with the original, fully-optimized program. We found that symbolic expression manipulation is important for most programs. Expression propagation and range analysis is important in few programs only, however they can affect these programs significantly. We also found that in all but one programs, a simpler form of range analysis – control range analysis – is sufficient.

Large Scale Simulations of Nanoelectronic devices with NEMO3-D on the Teragrid

This paper describes recent progress in large scale numerical simulations for computational nano-electronics using the NEMO3-D package. NEMO3-D is a parallel analysis tool for nano-electronic devices such as quantum dots. The atomistic model used in NEMO3-D leads to large scale computations in two main phases: strain and electronic structure. This paper focuses primarily on the electronic structure phase of the computations. The eigenvalue problem associated with the Hamiltonian matrix is challenging for a number of reasons: (i) the need for very large scale, 100 million to one billion unknowns (ii) the desired eigenvalues (along with the associated eigenvectors) lie in the interior of the spectrum and (iii) the eigenvalues are often degenerate. New results on the performance and scalability of NEMO3-D are presented, on advanced parallel architectures, including Teragrid resources. Results presented here were obtained with runs on up to 192 processors, for systems with 40 million atoms. We also report on on-going work to incorporate new advanced algorithms into NEMO3-D. We describe how the NEMO3-D code has been linked to the Teragrid through the NanoHub

Development of Physiologically Human-Relevant in vitro Brain Environment with Brain Decellularized Extracellular Matrix

In vitro model for neurodegenerative disease research has emerged to supplement the limitation of existing clinical in vivo model, regarding to recapitulation of human disease pathophysiology. However, the conventional in vitro models like 2D cell culture on a dish are restricted to mimic physiological environment, including components of extracellular matrix and stiffness of the native brain, which are lack in artificial culture materials. The features are essential to reproduce pathophysiology and biological characteristics of the disease models. In order to reconstruct the physiological native brain environment on 3D in vitro, we developed a brain decellularized extracellular matrix (BdECM) bioink. In brief, we isolated a porcine brain and washed the tissue with various solutions to remove cellular components and maintain extracellular matrix of the brain. Proteomics analysis showed that BdECM has protein components of basal lamina in the brain, including collagen and laminin. We also performed rheological analysis for solubilized BdECM bioink to find physiologically similar stiffness to the native brain. In addition to that, neural cells were employed to see the biological potential for 3D microenvironment. Microglia and neural progenitor cells were cultured on BdECM bioink. The cells showed enough cell viability and no immune response for microglia, confirmed with microglia activation marker. We have successfully developed BdECM to recapitulate physiological environment of the native brain, verifying stiffness and components analysis. We will apply the material for reconstruction of native brain environment and disease research.2

A Nano-electronics Simulator for Petascale Computing: From NEMO to OMEN

The rapid progress in nanofabrication technologies has led to the emergence of new classes of nano-devices, in which the quantum nature of charge carriers dominates the device properties and performance. The need for atomistic-level modeling is particularly clear in studies of quantum dots. Quantum dots are solid-state structures capable of trapping charge carriers so that their wave functions become fully spatially localized, and their energy spectra consist of wellseparated, discrete levels. Existing nanofabrication techniques make it possible to manufacture quantum dots in a variety of types and sizes [1]. Among them, semiconductor quantum dots grown by self-assembly (SADs), trapping electrons as well as holes, are of particular importance in quantum optics, since they can be used as detectors of infrared radiation [2], optical memories [3], single photon sources [4]. Arrays of quantum-mechanically coupled SADs can also be used as optically active regions in high-efficiency, room-temperature lasers [5]