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UNIT 8B a Full Adder
UNIT 8B Computer Organization: Levels of Abstraction 15110 Principles of Computing, 1 Carnegie Mellon University - CORTINA A Full Adder C ABCin Cout S in 0 0 0 A 0 0 1 0 1 0 B 0 1 1 1 0 0 1 0 1 C S out 1 1 0 1 1 1 15110 Principles of Computing, 2 Carnegie Mellon University - CORTINA 1 A Full Adder C ABCin Cout S in 0 0 0 0 0 A 0 0 1 0 1 0 1 0 0 1 B 0 1 1 1 0 1 0 0 0 1 1 0 1 1 0 C S out 1 1 0 1 0 1 1 1 1 1 ⊕ ⊕ S = A B Cin ⊕ ∧ ∨ ∧ Cout = ((A B) C) (A B) 15110 Principles of Computing, 3 Carnegie Mellon University - CORTINA Full Adder (FA) AB 1-bit Cout Full Cin Adder S 15110 Principles of Computing, 4 Carnegie Mellon University - CORTINA 2 Another Full Adder (FA) http://students.cs.tamu.edu/wanglei/csce350/handout/lab6.html AB 1-bit Cout Full Cin Adder S 15110 Principles of Computing, 5 Carnegie Mellon University - CORTINA 8-bit Full Adder A7 B7 A2 B2 A1 B1 A0 B0 1-bit 1-bit 1-bit 1-bit ... Cout Full Full Full Full Cin Adder Adder Adder Adder S7 S2 S1 S0 AB 8 ⁄ ⁄ 8 C 8-bit C out FA in ⁄ 8 S 15110 Principles of Computing, 6 Carnegie Mellon University - CORTINA 3 Multiplexer (MUX) • A multiplexer chooses between a set of inputs. D1 D 2 MUX F D3 D ABF 4 0 0 D1 AB 0 1 D2 1 0 D3 1 1 D4 http://www.cise.ufl.edu/~mssz/CompOrg/CDAintro.html 15110 Principles of Computing, 7 Carnegie Mellon University - CORTINA Arithmetic Logic Unit (ALU) OP 1OP 0 Carry In & OP OP 0 OP 1 F 0 0 A ∧ B 0 1 A ∨ B 1 0 A 1 1 A + B http://cs-alb-pc3.massey.ac.nz/notes/59304/l4.html 15110 Principles of Computing, 8 Carnegie Mellon University - CORTINA 4 Flip Flop • A flip flop is a sequential circuit that is able to maintain (save) a state. -
MMP) Is One of the Cornerstones in the Clas- Sification Theory of Complex Projective Varieties
MINIMAL MODELS FOR KÄHLER THREEFOLDS ANDREAS HÖRING AND THOMAS PETERNELL Abstract. Let X be a compact Kähler threefold that is not uniruled. We prove that X has a minimal model. 1. Introduction The minimal model program (MMP) is one of the cornerstones in the clas- sification theory of complex projective varieties. It is fully developed only in dimension 3, despite tremendous recent progress in higher dimensions, in particular by [BCHM10]. In the analytic Kähler situation the basic methods from the MMP, such as the base point free theorem, fail. Nevertheless it seems reasonable to expect that the main results should be true also in this more general context. The goal of this paper is to establish the minimal model program for Kähler threefolds X whose canonical bundle KX is pseudoeffective. To be more specific, we prove the following result: 1.1. Theorem. Let X be a normal Q-factorial compact Kähler threefold with at most terminal singularities. Suppose that KX is pseudoeffective or, equivalently, that X is not uniruled. Then X has a minimal model, i.e. there exists a MMP X 99K X′ such that KX′ is nef. In our context a complex space is said to be Q-factorial if every Weil ⊗m ∗∗ divisor is Q-Cartier and a multiple (KX ) of the canonical sheaf KX is locally free. If X is a projective threefold, Theorem 1.1 was estab- lished by the seminal work of Kawamata, Kollár, Mori, Reid and Shokurov [Mor79, Mor82, Rei83, Kaw84a, Kaw84b, Kol84, Sho85, Mor88, KM92]. Based on the deformation theory of rational curves on smooth threefolds [Kol91b, Kol96], Campana and the second-named author started to inves- tigate the existence of Mori contractions [CP97, Pet98, Pet01]. -
MINIMAL MODEL THEORY of NUMERICAL KODAIRA DIMENSION ZERO Contents 1. Introduction 1 2. Preliminaries 3 3. Log Minimal Model Prog
MINIMAL MODEL THEORY OF NUMERICAL KODAIRA DIMENSION ZERO YOSHINORI GONGYO Abstract. We prove the existence of good minimal models of numerical Kodaira dimension 0. Contents 1. Introduction 1 2. Preliminaries 3 3. Log minimal model program with scaling 7 4. Zariski decomposition in the sense of Nakayama 9 5. Existence of minimal model in the case where º = 0 10 6. Abundance theorem in the case where º = 0 12 References 14 1. Introduction Throughout this paper, we work over C, the complex number ¯eld. We will make use of the standard notation and de¯nitions as in [KM] and [KMM]. The minimal model conjecture for smooth varieties is the following: Conjecture 1.1 (Minimal model conjecture). Let X be a smooth pro- jective variety. Then there exists a minimal model or a Mori ¯ber space of X. This conjecture is true in dimension 3 and 4 by Kawamata, Koll¶ar, Mori, Shokurov and Reid (cf. [KMM], [KM] and [Sho2]). In the case where KX is numerically equivalent to some e®ective divisor in dimen- sion 5, this conjecture is proved by Birkar (cf. [Bi1]). When X is of general type or KX is not pseudo-e®ective, Birkar, Cascini, Hacon Date: 2010/9/21, version 4.03. 2000 Mathematics Subject Classi¯cation. 14E30. Key words and phrases. minimal model, the abundance conjecture, numerical Kodaira dimension. 1 2 YOSHINORI GONGYO and McKernan prove Conjecture 1.1 for arbitrary dimension ([BCHM]). Moreover if X has maximal Albanese dimension, Conjecture 1.1 is true by [F2]. In this paper, among other things, we show Conjecture 1.1 in the case where º(KX ) = 0 (for the de¯nition of º, see De¯nition 2.6): Theorem 1.2. -
UC Berkeley UC Berkeley Electronic Theses and Dissertations
UC Berkeley UC Berkeley Electronic Theses and Dissertations Title Cox Rings and Partial Amplitude Permalink https://escholarship.org/uc/item/7bs989g2 Author Brown, Morgan Veljko Publication Date 2012 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California Cox Rings and Partial Amplitude by Morgan Veljko Brown A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Mathematics in the Graduate Division of the University of California, BERKELEY Committee in charge: Professor David Eisenbud, Chair Professor Martin Olsson Professor Alistair Sinclair Spring 2012 Cox Rings and Partial Amplitude Copyright 2012 by Morgan Veljko Brown 1 Abstract Cox Rings and Partial Amplitude by Morgan Veljko Brown Doctor of Philosophy in Mathematics University of California, BERKELEY Professor David Eisenbud, Chair In algebraic geometry, we often study algebraic varieties by looking at their codimension one subvarieties, or divisors. In this thesis we explore the relationship between the global geometry of a variety X over C and the algebraic, geometric, and cohomological properties of divisors on X. Chapter 1 provides background for the results proved later in this thesis. There we give an introduction to divisors and their role in modern birational geometry, culminating in a brief overview of the minimal model program. In chapter 2 we explore criteria for Totaro's notion of q-amplitude. A line bundle L on X is q-ample if for every coherent sheaf F on X, there exists an integer m0 such that m ≥ m0 implies Hi(X; F ⊗ O(mL)) = 0 for i > q. -
With Extreme Scale Computing the Rules Have Changed
With Extreme Scale Computing the Rules Have Changed Jack Dongarra University of Tennessee Oak Ridge National Laboratory University of Manchester 11/17/15 1 • Overview of High Performance Computing • With Extreme Computing the “rules” for computing have changed 2 3 • Create systems that can apply exaflops of computing power to exabytes of data. • Keep the United States at the forefront of HPC capabilities. • Improve HPC application developer productivity • Make HPC readily available • Establish hardware technology for future HPC systems. 4 11E+09 Eflop/s 362 PFlop/s 100000000100 Pflop/s 10000000 10 Pflop/s 33.9 PFlop/s 1000000 1 Pflop/s SUM 100000100 Tflop/s 166 TFlop/s 1000010 Tflop /s N=1 1 Tflop1000/s 1.17 TFlop/s 100 Gflop/s100 My Laptop 70 Gflop/s N=500 10 59.7 GFlop/s 10 Gflop/s My iPhone 4 Gflop/s 1 1 Gflop/s 0.1 100 Mflop/s 400 MFlop/s 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2015 1 Eflop/s 1E+09 420 PFlop/s 100000000100 Pflop/s 10000000 10 Pflop/s 33.9 PFlop/s 1000000 1 Pflop/s SUM 100000100 Tflop/s 206 TFlop/s 1000010 Tflop /s N=1 1 Tflop1000/s 1.17 TFlop/s 100 Gflop/s100 My Laptop 70 Gflop/s N=500 10 59.7 GFlop/s 10 Gflop/s My iPhone 4 Gflop/s 1 1 Gflop/s 0.1 100 Mflop/s 400 MFlop/s 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2015 1E+10 1 Eflop/s 1E+09 100 Pflop/s 100000000 10 Pflop/s 10000000 1 Pflop/s 1000000 SUM 100 Tflop/s 100000 10 Tflop/s N=1 10000 1 Tflop/s 1000 100 Gflop/s N=500 100 10 Gflop/s 10 1 Gflop/s 1 100 Mflop/s 0.1 1996 2002 2020 2008 2014 1E+10 1 Eflop/s 1E+09 100 Pflop/s 100000000 10 Pflop/s 10000000 1 Pflop/s 1000000 SUM 100 Tflop/s 100000 10 Tflop/s N=1 10000 1 Tflop/s 1000 100 Gflop/s N=500 100 10 Gflop/s 10 1 Gflop/s 1 100 Mflop/s 0.1 1996 2002 2020 2008 2014 • Pflops (> 1015 Flop/s) computing fully established with 81 systems. -
The Minimal Model Program for B-Log Canonical Divisors and Applications
THE MINIMAL MODEL PROGRAM FOR B-LOG CANONICAL DIVISORS AND APPLICATIONS DANIEL CHAN, KENNETH CHAN, LOUIS DE THANHOFFER DE VOLCSEY,¨ COLIN INGALLS, KELLY JABBUSCH, SANDOR´ J KOVACS,´ RAJESH KULKARNI, BORIS LERNER, BASIL NANAYAKKARA, SHINNOSUKE OKAWA, AND MICHEL VAN DEN BERGH Abstract. We discuss the minimal model program for b-log varieties, which is a pair of a variety and a b-divisor, as a natural generalization of the minimal model program for ordinary log varieties. We show that the main theorems of the log MMP work in the setting of the b-log MMP. If we assume that the log MMP terminates, then so does the b- log MMP. Furthermore, the b-log MMP includes both the log MMP and the equivariant MMP as special cases. There are various interesting b-log varieties arising from different objects, including the Brauer pairs, or “non-commutative algebraic varieties which are finite over their centres”. The case of toric Brauer pairs is discussed in further detail. Contents 1. Introduction 1 2. Minimal model theory with boundary b-divisors 4 2.A. Recap on b-divisors 4 2.B. b-discrepancy 8 3. The Minimal Model Program for b-log varieties 11 3.A. Fundamental theorems for b-log varieties 13 4. Toroidal b-log varieties 15 4.A. Snc stratifications 15 4.B. Toric b-log varieties 17 4.C. Toroidal b-log varieties 19 5. Toric Brauer Classes 21 References 24 1. Introduction arXiv:1707.00834v2 [math.AG] 5 Jul 2017 Let k be an algebraically closed field of characteristic zero. Let K be a field, finitely generated over k. -
Effective Bounds for the Number of MMP-Series of a Smooth Threefold
EFFECTIVE BOUNDS FOR THE NUMBER OF MMP-SERIES OF A SMOOTH THREEFOLD DILETTA MARTINELLI Abstract. We prove that the number of MMP-series of a smooth projective threefold of positive Kodaira dimension and of Picard number equal to three is at most two. 1. Introduction Establishing the existence of minimal models is one of the first steps towards the birational classification of smooth projective varieties. More- over, starting from dimension three, minimal models are known to be non-unique, leading to some natural questions such as: does a variety admit a finite number of minimal models? And if yes, can we fix some parameters to bound this number? We have to be specific in defining what we mean with minimal mod- els and how we count their number. A marked minimal model of a variety X is a pair (Y,φ), where φ: X 99K Y is a birational map and Y is a minimal model. The number of marked minimal models, up to isomorphism, for a variety of general type is finite [BCHM10, KM87]. When X is not of general type, this is no longer true, [Rei83, Example 6.8]. It is, however, conjectured that the number of minimal models up to isomorphism is always finite and the conjecture is known in the case of threefolds of positive Kodaira dimension [Kaw97]. In [MST16] it is proved that it is possible to bound the number of marked minimal models of a smooth variety of general type in terms of the canonical volume. Moreover, in dimension three Cascini and Tasin [CT18] bounded the volume using the Betti numbers. -
Theoretical Peak FLOPS Per Instruction Set on Modern Intel Cpus
Theoretical Peak FLOPS per instruction set on modern Intel CPUs Romain Dolbeau Bull – Center for Excellence in Parallel Programming Email: [email protected] Abstract—It used to be that evaluating the theoretical and potentially multiple threads per core. Vector of peak performance of a CPU in FLOPS (floating point varying sizes. And more sophisticated instructions. operations per seconds) was merely a matter of multiplying Equation2 describes a more realistic view, that we the frequency by the number of floating-point instructions will explain in details in the rest of the paper, first per cycles. Today however, CPUs have features such as vectorization, fused multiply-add, hyper-threading or in general in sectionII and then for the specific “turbo” mode. In this paper, we look into this theoretical cases of Intel CPUs: first a simple one from the peak for recent full-featured Intel CPUs., taking into Nehalem/Westmere era in section III and then the account not only the simple absolute peak, but also the full complexity of the Haswell family in sectionIV. relevant instruction sets and encoding and the frequency A complement to this paper titled “Theoretical Peak scaling behavior of current Intel CPUs. FLOPS per instruction set on less conventional Revision 1.41, 2016/10/04 08:49:16 Index Terms—FLOPS hardware” [1] covers other computing devices. flop 9 I. INTRODUCTION > operation> High performance computing thrives on fast com- > > putations and high memory bandwidth. But before > operations => any code or even benchmark is run, the very first × micro − architecture instruction number to evaluate a system is the theoretical peak > > - how many floating-point operations the system > can theoretically execute in a given time. -
Resolutions of Quotient Singularities and Their Cox Rings Extended Abstract
RESOLUTIONS OF QUOTIENT SINGULARITIES AND THEIR COX RINGS EXTENDED ABSTRACT MAKSYMILIAN GRAB The resolution of singularities allows one to modify an algebraic variety to obtain a nonsingular variety sharing many properties with the original, singular one. The study of nonsingular varieties is an easier task, due to their good geometric and topological properties. By the theorem of Hironaka [29] it is always possible to resolve singularities of a complex algebraic variety. The dissertation is concerned with resolutions of quotient singularities over the field C of complex numbers. Quotient singularities are the singularities occuring in quotients of algebraic varieties by finite group actions. A good local model of such a singularity n is a quotient C /G of an affine space by a linear action of a finite group. The study of quotient singularities dates back to the work of Hilbert and Noether [28], [40], who proved finite generation of the ring of invariants of a linear finite group action on a ring of polynomials. Other important classical result is due to Chevalley, Shephard and Todd [10], [46], who characterized groups G that yield non-singular quotients. Nowa- days, quotient singularities form an important class of singularities, since they are in some sense typical well-behaved (log terminal) singularities in dimensions two and three (see [35, Sect. 3.2]). Methods of construction of algebraic varieties via quotients, such as the classical Kummer construction, are being developed and used in various contexts, including higher dimensions, see [1] and [20] for a recent example of application. In the well-studied case of surfaces every quotient singularity admits a unique minimal resolution. -
Misleading Performance Reporting in the Supercomputing Field David H
Misleading Performance Reporting in the Supercomputing Field David H. Bailey RNR Technical Report RNR-92-005 December 1, 1992 Ref: Scientific Programming, vol. 1., no. 2 (Winter 1992), pg. 141–151 Abstract In a previous humorous note, I outlined twelve ways in which performance figures for scientific supercomputers can be distorted. In this paper, the problem of potentially mis- leading performance reporting is discussed in detail. Included are some examples that have appeared in recent published scientific papers. This paper also includes some pro- posed guidelines for reporting performance, the adoption of which would raise the level of professionalism and reduce the level of confusion in the field of supercomputing. The author is with the Numerical Aerodynamic Simulation (NAS) Systems Division at NASA Ames Research Center, Moffett Field, CA 94035. 1 1. Introduction Many readers may have read my previous article “Twelve Ways to Fool the Masses When Giving Performance Reports on Parallel Computers” [5]. The attention that this article received frankly has been surprising [11]. Evidently it has struck a responsive chord among many professionals in the field who share my concerns. The following is a very brief summary of the “Twelve Ways”: 1. Quote only 32-bit performance results, not 64-bit results, and compare your 32-bit results with others’ 64-bit results. 2. Present inner kernel performance figures as the performance of the entire application. 3. Quietly employ assembly code and other low-level language constructs, and compare your assembly-coded results with others’ Fortran or C implementations. 4. Scale up the problem size with the number of processors, but don’t clearly disclose this fact. -
Intel Xeon & Dgpu Update
Intel® AI HPC Workshop LRZ April 09, 2021 Morning – Machine Learning 9:00 – 9:30 Introduction and Hardware Acceleration for AI OneAPI 9:30 – 10:00 Toolkits Overview, Intel® AI Analytics toolkit and oneContainer 10:00 -10:30 Break 10:30 - 12:30 Deep dive in Machine Learning tools Quizzes! LRZ Morning Session Survey 2 Afternoon – Deep Learning 13:30 – 14:45 Deep dive in Deep Learning tools 14:45 – 14:50 5 min Break 14:50 – 15:20 OpenVINO 15:20 - 15:45 25 min Break 15:45 – 17:00 Distributed training and Federated Learning Quizzes! LRZ Afternoon Session Survey 3 INTRODUCING 3rd Gen Intel® Xeon® Scalable processors Performance made flexible Up to 40 cores per processor 20% IPC improvement 28 core, ISO Freq, ISO compiler 1.46x average performance increase Geomean of Integer, Floating Point, Stream Triad, LINPACK 8380 vs. 8280 1.74x AI inference increase 8380 vs. 8280 BERT Intel 10nm Process 2.65x average performance increase vs. 5-year-old system 8380 vs. E5-2699v4 Performance varies by use, configuration and other factors. Configurations see appendix [1,3,5,55] 5 AI Performance Gains 3rd Gen Intel® Xeon® Scalable Processors with Intel Deep Learning Boost Machine Learning Deep Learning SciKit Learn & XGBoost 8380 vs 8280 Real Time Inference Batch 8380 vs 8280 Inference 8380 vs 8280 XGBoost XGBoost up to up to Fit Predict Image up up Recognition to to 1.59x 1.66x 1.44x 1.30x Mobilenet-v1 Kmeans Kmeans up to Fit Inference Image up to up up Classification to to 1.52x 1.56x 1.36x 1.44x ResNet-50-v1.5 Linear Regression Linear Regression Fit Inference Language up to up to up up Processing to 1.44x to 1.60x 1.45x BERT-large 1.74x Performance varies by use, configuration and other factors. -
Chapter 4 Data-Level Parallelism in Vector, SIMD, and GPU Architectures
Computer Architecture A Quantitative Approach, Fifth Edition Chapter 4 Data-Level Parallelism in Vector, SIMD, and GPU Architectures Copyright © 2012, Elsevier Inc. All rights reserved. 1 Contents 1. SIMD architecture 2. Vector architectures optimizations: Multiple Lanes, Vector Length Registers, Vector Mask Registers, Memory Banks, Stride, Scatter-Gather, 3. Programming Vector Architectures 4. SIMD extensions for media apps 5. GPUs – Graphical Processing Units 6. Fermi architecture innovations 7. Examples of loop-level parallelism 8. Fallacies Copyright © 2012, Elsevier Inc. All rights reserved. 2 Classes of Computers Classes Flynn’s Taxonomy SISD - Single instruction stream, single data stream SIMD - Single instruction stream, multiple data streams New: SIMT – Single Instruction Multiple Threads (for GPUs) MISD - Multiple instruction streams, single data stream No commercial implementation MIMD - Multiple instruction streams, multiple data streams Tightly-coupled MIMD Loosely-coupled MIMD Copyright © 2012, Elsevier Inc. All rights reserved. 3 Introduction Advantages of SIMD architectures 1. Can exploit significant data-level parallelism for: 1. matrix-oriented scientific computing 2. media-oriented image and sound processors 2. More energy efficient than MIMD 1. Only needs to fetch one instruction per multiple data operations, rather than one instr. per data op. 2. Makes SIMD attractive for personal mobile devices 3. Allows programmers to continue thinking sequentially SIMD/MIMD comparison. Potential speedup for SIMD twice that from MIMID! x86 processors expect two additional cores per chip per year SIMD width to double every four years Copyright © 2012, Elsevier Inc. All rights reserved. 4 Introduction SIMD parallelism SIMD architectures A. Vector architectures B. SIMD extensions for mobile systems and multimedia applications C.