Triptolide sensitizes cancer cells to nucleoside DNA methyltransferase inhibitors through inhibition of DCTPP1-mediated cell-intrinsic resistance
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Liu, Jianyong↗
Engineering topics
Mishra, Alok
Publications and source records attributed to Mishra, Alok.
Quantum optimization algorithms: Energetic implications
Since the dawn of quantum computing (QC), theoretical developments like Shor's algorithm proved the conceptual superiority of QC over traditional computing. However, such quantum supremacy claims are difficult to achieve in practice because of the technical challenges of realizing noiseless qubits. In the near future, QC applications will need to rely on noisy quantum devices that offload part of their work to classical devices. One way to achieve this is by using parameterized quantum circuits in optimization or even in machine learning tasks. The energy requirements of quantum algorithms have not yet been studied extensively. Here in this article, we explore several optimization algorithms using both theoretical insights and numerical experiments to understand their impact on energy consumption. Specifically, we highlight why and how algorithms like quantum natural gradient descent, simultaneous perturbation stochastic approximations or circuit learning methods, are at least 2x to 4x more energy efficient than their classical counterparts; why feedback-based quantum optimization is energy-inefficient; and how techniques like Rosalin can improve the energy efficiency of other algorithms by a factor of ≥2 0 x. Finally, we use the NchooseK high-level programming model to run optimization problems on both gate-based quantum computers and quantum annealers. Empirical data indicate that these optimization problems run faster, have better success rates, and consume less energy on quantum annealers than on their gate-based counterparts.
97 MATHEMATICS AND COMPUTING↗
COMPOFF: A Compiler Cost model using Machine Learning to predict the Cost of OpenMP Offloading
The HPC industry is inexorably moving towards an era of extremely heterogeneous architectures, with more devices configured on any given HPC platform and potentially more kinds of devices, some of them highly specialized. Writing a separate code suitable for each target system for a given HPC application is not practical. The better solution is to use directive-based parallel programming models such as OpenMP. OpenMP provides a number of options for offloading a piece of code to devices like GPUs. To select the best option from such options during compilation, most modern compilers use analytical models to estimate the cost of executing the original code and the different offloading code variants. Building such an analytical model for compilers is a difficult task that necessitates a lot of effort on the part of a compiler engineer. Recently, machine learning techniques have been successfully applied to build cost models for a variety of compiler optimization problems. In this paper, we present COMPOFF, a cost model which uses the multi-layer perceptrons to statically estimates the Cost of OpenMP OFFloading. We used six different transformations on a parallel code of Wilson Dslash Operator to support GPU offloading, and we predicted their cost of execution on different GPUs using COMPOFF during compile time. Our results show that this model can predict offloading costs with a root mean squared error in prediction of less than 0.5 seconds. Our preliminary findings indicate that this work will make it much easier and faster for scientists and compiler developers to port legacy HPC applications that use OpenMP to new heterogeneous computing environment.
97 MATHEMATICS AND COMPUTING↗
Wear Behavior of an Ultra-High-Strength Eutectoid Steel
Wear behavior of an ultra-high-strength AISI 1080 steel developed through incomplete austenitization-based combined cyclic heat treatment is investigated in comparison with annealed and conventional hardened and tempered conditions against an alumina disk (sliding speed = 1 m s{sup −1}) using a pin-on-disk tribometer at a load range of 7.35-14.7 N. On a gross scale, the mechanism of surface damage involves adhesive wear coupled with abrasive wear (microcutting effects in particular) at lower loads. At higher loads, mainly the abrasive wear (both microcutting and microploughing mechanisms) and evolution of adherent oxide are observed. Besides, microhardness of matrix increases with load indicating substantial strain hardening during wear test. The rate of overall wear is found to increase with load. As-received annealed steel with the lowest initial hardness suffers from severe abrasive wear, thereby exhibiting the highest wear loss. Such a severe wear loss is not observed in conventional hardened and tempered and combined cyclic heat treatment conditions. Combined cyclic heat-treated steel exhibits the greatest wear resistance (lowest wear loss) due to its initial high hardness and evolution of hard abrasion-resistant tribolayer during wear test at higher load.
36 MATERIALS SCIENCE↗
Benchmarking and Evaluating Unified Memory for OpenMP GPU Offloading
Here, the latest OpenMP standard offers automatic device offloading capabilities which facilitate GPU programming. Despite this, there remain many challenges. One of these is the unified memory feature introduced in recent GPUs. GPUs in current and future HPC systems have enhanced support for unified memory space. In such systems, CPU and GPU can access each other's memory transparently, that is, the data movement is managed automatically by the underlying system software and hardware. Memory over subscription is also possible in these systems. However, there is a significant lack of knowledge about how this mechanism will perform, and how programmers should use it. We have modified several benchmarks codes, in the Rodinia benchmark suite, to study the behavior of OpenMP accelerator extensions and have used them to explore the impact of unified memory in an OpenMP context. We moreover modified the open source LLVM compiler to allow OpenMP programs to exploit unified memory. The results of our evaluation reveal that, while the performance of unified memory is comparable with that of normal GPU offloading for benchmarks with little data reuse, it suffers from significant overhead when GPU memory is over subcribed for benchmarks with large amount of data reuse. Based on these results, we provide several guidelines for programmers to achieve better performance with unified memory.
97 MATHEMATICS AND COMPUTING↗