Supercomputers in Theoretical and Experimental Science

I. Introduction.- Science, Simulation and Supercomputers.- 1. Introduction.- 2. A far-reaching manifesto.- 3. The scientific progress.- 4. A revolution in scale.- 5. Supercomputers, who needs them?.- 6. Some final remarks.- 7. References.- II. SIMD Supercomputers.- Vector Processing on CRAY-1 and CRAY X-MP.- 1. Introduction.- 2. Principles of CRAY-1 architecture.- 3. Architecture of CRAY X-MP.- 4. Chaining.- 5. Execution sequence of Fortran kernels on CRAY-1 and CRAY X-MP.- 6. CFT - the CRAY Fortran compiler.- 7. Optimizing Fortran programs for the CRAY.- 8. Conclusion.- 9. Acknowledgments.- 10. References.- Vector Processing Techniques on CYBER 205.- Abstract.- 1. Introduction.- 1.1. Horizontal parallelism.- 1.2. Vertical parallelism.- 2. Some remarks on CYBER 205 data operators.- 2.1. Scalar method.- 2.2. The single-vector method.- 2.3. The use of control vectors.- 2.4. VXTOV (scatter-gather) technique.- 2.5. The compress method.- 3. Numerical examples.- (a) Iterative Gaussian method of Traub.- (b) Vectorized Jacobi method.- (c) SOR-method.- 4. References.- Programming Discipline on Vector Computers: "Vectors" as a Data Type and Vector Algorithms.- 1. The vector machine.- 1.1. A look at CRAY-1 performances.- 1.2. Which operations benefit from vector speed, and why?.- 1.2.1. Extensions.- 1.2.2. Arrays of processors and pipe-lines.- 1.2.3. Memory access,regular storage.- 1.3. The 'vector machine' concept.- 2. Vector algorithms.- 2.1. Linear recurrence.- 2.1.1. 'Iterative' approach: recursive doubling.- 2.1.2. 'Recursive' approach: cyclic reduction.- 2.1.3. Discussion.- 2.2. Linear algebra.- 2.3. The 'total' reduction problem.- 2.3.1. Statement of the problem.- 2.3.2. Examples of problems of the 'total reduction' family.- 2.3.3. Vector methods for problem (52).- 2.3.4. A pipeline for the reduction problem.- 2.4. Sum of the components, and dotproduct, on CRAY-1.- 3. References.- III. MIMD Supercomputers.- MIMD Supercomputers for Numerical Applications.- 1. Vector computers - parallel computers.- 1.1. Architectural type.- 1.2. Programming a supercomputer.- 2. Numerical applications on MIMD parallel supercomputers.- 2.1. Parallel approach to solve numerical problems.- 2.2. Programming aspects on MIMD supercomputers.- 2.3. Program optimization.- 3. Architectures MIMD and performance.- 3.1. Software concepts.- 3.2. Hardware concepts.- 3.3. Loosely coupled MIMD architectures.- 3.4. Task sequencing language.- 3.4.1. Different task types.- 3.4.2. DECLARE and ALLOCATE parts.- 3.4.3. Sequencing expressions.- 3.5. Simulation results.- 3.5.1. Characteristics of the simulator.- 3.5.2. Performances.- 4. Implementing and concepts.- 4.1. Multi-array-processor confiruration.- 4.2. Parellel language implementation.- 4.3. Programming parallel applications.- 4.4. Configuration evolution: performance evaluation.- 5. Conclusion.- 6. Acknowledgments.- 7. References.- IV. Applications.- Supercomputers in Theoretical High-Energy Physics.- Abstract.- 1. Introduction.- 2. Computational physics.- 3. The CYBER 205.- 4. The Monte-Carlo method for lattice gauge theory.- 5. Acknowledgment.- 6. References.- The Use of a Vector Computer in Ab-Initio Phonon Calculations in Semiconductors.- 1. Introduction.- 2. Theory.- 3. Computational procedures.- 3.1. The electron-ion potential.- 3.2. The electronic band structure.- 3.3. The total crystal energy.- 3.4. Vectorization of the codes.- 3.4.1. Matrix multiplication.- 3.4.2. Matrix inversion.- 3.4.3. Matrix diagonalization.- 3.4.4. The polarizability matrix.- 4. Discussion of the results.- 4.1. Convergence as a function of the dimension of e.- 4.2. Convergence as a function of the number of plane waves in the basis.- 4.3. The dispersion curves.- 5. Conclusions.- 6. Acknowledgments.- 7. References.- Numerical Solution of the Time-Dependent Hartree-Fock Equation in the Electron Gas.- 1. Introduction.- 2. The homogeneous electron gas model.- 2.1. Energy loss of X-rays and fast electrons.- 2.2. The effective potentia