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- 1. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix The 17th International Symposium on Operations Research in Slovenia (SOR ’23) 20th – 22nd September 2023, Hotel Astoria, Bled, Slovenia Friday, September 22, 2023, at 11.30 in Hall I: Special Session 6, Industry & Society 5.0: Optimization and Learning in Human and Industrial Environments Organized by the Slovenian Society Informatika, Section of Operations Research Solving 100-Digit Challenge with Score 100 by Extended Running Time and Parallel Benchmarking 22 September 2023 @ SOR23, Bled Aleš Zamuda <ales.zamuda@um.si> Acknowledgement: this work is supported by ARRS programme P2-0041 and EU project no. 957407. 10000 1x106 1x108 1x1010 1x1012 0 1 2 3 4 5 6 7 8 9 FES Digits f1 f2 f3 f4 f5 f6 f7 f8 f9 f10 Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time & Parallel Bench. 1/64
- 2. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix Introduction & Outline: Aims of this Talk 1 (5 minutes) Part I: Background – Optimization Algorithms and 100-Digit Challenge 2 (5 minutes) Part II: Method: DISHchain3e+12 Algorithm 3 (2 minutes) Part III: Results 4 (1 minutes) Part IV: Conclusion with Takeaways 5 (1 minute) Questions, Misc 6 (Appendix) Business, Marketing Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time & Parallel Bench. 2/64
- 3. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix The 17th International Symposium on Operations Research in Slovenia (SOR ’23) 20th – 22nd September 2023, Hotel Astoria, Bled, Slovenia Friday, September 22, 2023, at 11.30 in Hall I: Special Session 6, Industry & Society 5.0: Optimization and Learning in Human and Industrial Environments Organized by the Slovenian Society Informatika, Section of Operations Research Solving 100-Digit Challenge with Score 100 by Extended Running Time and Parallel Benchmarking — I. Background: Optimization, 100-Digit Challenge — Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time & Parallel Bench. 3/64
- 4. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix Optimization Beginnings - Optimization is ”Everywhere” • Time: optimizing distribution of what is matter and what is not (anti-matter), what is energy and what is not (dark energy), etc.: according to the function of Nature, the system is propelled through optimizing its constituents dynamics. • Organic systems combination and propulsion: life (optimization). • Optimality and optimization modeling (human builds tools). • Describing ways of acchieving optimality. • Mathematical optimization procedure deﬁned (Kepler). • Stepping towards optimum (Newton), gradient method (Lagrange). • Multi-objective optimization (Pareto): • meta-criterion (A ⪯ B): make criteria ordered by dominance. f′ (x) = ∆f(x) ∆x , f∗ (x) = f(x) + ∆xf′ (x). 1 2 2 f x x 1 f ( ) A B C D f x f(B) (A) f f(D) 0 0 E f(E) F G f (C) f f(F) (G) Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time & Parallel Bench. 4/64
- 5. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix Introduction to Optimization Algorithms and Mathematical Programming • Global optimization, mathematical programming, digital computers. • Computing Machines + Intelligence = Artiﬁcial Intelligence. • Computational Intelligence. • Simplistic numerical optimization algorithms: hill climbing, Nelder-Mead, supervised random search, simulated annealing, tabu search. • Optimization: constrained, inseparable, multi-modal, multi-objective, dynamic, noisy, high dimensional/large-scale/big-data, deceptive, etc. • multi-objective: f(x)): Pareto optimal approximation set. Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time & Parallel Bench. 5/64
- 6. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix Evolutionary Computation and Algorithms • Evolution theory: C. Darwin (1859), Weismann, Mendel. • Popularization: darwinism (Huxley), neodarwinism (Romanes). • Generational: reproduction, mutation, competition, selection. • Evolutionary Computation: Evolutionary Algorithms (EAs) • population generations (reproduction-based), • mutation, crossover, selection (evolutionary operators), • EAs comprised of different mechanisms. • These algorithms share several common mechanisms/operators, • good conﬁgured DEs were prevalent at the winning positions of all (CEC, including ICEC 1996) competitions on optimization. Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time & Parallel Bench. 6/64
- 7. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix Evolutionary Computation and Algorithms: Given Names • Simulated Annealing (SA), • Tabu Search (TS), • Genetic Algorithms (GA), • Genetic Programming (GP), • Evolutionary Programming (EP), • Memetic Algorithms (MA), • Evolution Strategy (ES), • Artiﬁcial Immune Systems (AIS), • Cultural Algorithms (CA) • Swarm Intelligence (SI), • Particle Swarm Optimization (PSO), • Fireﬂy Algorithm (FA), • Ant Colony Optimization (ACO), • Artiﬁcial Bee Colony (ABC), • Cuckoo Search (CS), • Artiﬁcial Weed Optimization (IWO), • Bacterial Foraging Optimization(BFO), • Estimation of Distribution Alg. (EDA), • Harmony Search (HS), • Gravitational Search Algorithm (GSA), • Biogeography-based Optimization(BBO), • Differential Evolution (DE) and its variants (jDE, L-SHADE, DISH), • ... and many more, including hybrids. Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time & Parallel Bench. 7/64
- 8. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix Range of Applications of the Optimization Algorithms • Meta-heuristics algorithms, applicable to: • (architectural) morphology (re)construction (vivo/technical), • artiﬁcial life: • modeling ecosystem and environmental living conditions, • e.g.: (automatic) procedural tree modeling, interactive ecosystem breeding. • pattern recognition, image processing, computer vision, • language/documents understanding, speech processing, • robotics, bioinformatics, chemical engineering, manufacturing, • oil search, nuclear plant safety, ﬁnance, electrical engineering, • energy, big data, data mining, security, ocean/space research, • systems of systems, ..., artiﬁcial intelligence. Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time & Parallel Bench. 8/64
- 9. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix Differential Evolution (DE) • A ﬂoating point encoding EA for global optimization over continuous spaces, • through generations, the evolution process improves population of vectors, • iteratively by combining a parent individual and several other individuals of the same population, using evolutionary operators. • We choose the strategy jDE/rand/1/bin • mutation: vi,G+1 = xr1,G + F × (xr2,G − xr3,G), • crossover: ui,j,G+1 = ( vi,j,G+1 if rand(0, 1) ≤ CR or j = jrand xi,j,G otherwise , • selection: xi,G+1 = ( ui,G+1 if f(ui,G+1) < f(xi,G) xi,G otherwise , Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time & Parallel Bench. 9/64
- 10. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix Algorithm DE 1: algorithm canonical algorithm DE/rand/1/bin (Storn, 1997) Require: f(x) – ﬁtness function; D, NP, G – DE control parameters Ensure: xbest – includes optimized parameters for the ﬁtness function 2: Uniform randomly initialize the population (xi,0, i = 1..NP); 3: for DE generation loop g (until g < G) do 4: for DE iteration loop i (for all vectors xi,g in current population) do 5: DE trial vector computation xi,g (mutation, crossover): 6: vi,g+1 = xr1,g + F × (xr2,g − xr3,g); 7: ui,j,g+1 = ( vi,j,g+1 if rand(0, 1) ≤ CR or j = jrand xi,j,g otherwise ; 8: DE selection using ﬁtness evaluation f(ui,G+1): 9: xi,g+1 = ( ui,g+1 if f(ui,g+1) < f(xi,g) xi,g otherwise ; 10: end for 11: end for 12: return best obtained vector (xbest); Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time & Parallel Bench. 10/64
- 11. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix Control Parameters Self-Adaptation • Through more suitable values of control parameters the search process exhibits a better convergence, • therefore the search converges faster to better solutions, which survive with greater probability and they create more offspring and propagate their control parameters • Recent study with cca. 10 million runs of SPSRDEMMS: A. Zamuda, J. Brest. Self-adaptive control parameters’ randomization frequency and propagations in differential evolution. Swarm and Evolutionary Computation, 2015, vol. 25C, pp. 72-99. DOI 10.1016/j.swevo.2015.10.007. – SWEVO 2015 RAMONA / SNIP 5.220 Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time & Parallel Bench. 11/64
- 12. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix Self-adaptive control parameters’ randomization frequency and propagations in differential evolution – Overview • Randomization frequency inﬂuences performance (SPSRDEMMS on right) • Suggesting values for different problems • 0.1 to 0.8 for τF, 0.05 to 0.25 for τCR • Empirical insight into operation of the randomization mechanism Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time & Parallel Bench. 12/64
- 13. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix Listing Some More DE-Family Algorithms Proposed • My algorithms (CEC – world championships on EAs): • SA-DE (CEC 2005: SO) – book chapter JCR, • MOjDE (CEC 2007: MO) – vs. DEMO 40/57 IR, 39/57 IH, • DEMOwSA (CEC 2007: MO) – rank #3, 53 citations, • DEwSAcc (CEC 2008: LSGO) – 63 citations, • DEMOwSA-SQP (CEC 2009: CMO) – rank #2, 47 citations, • DECMOSA-SQP (CEC 2009: CMO) – rank #3 at 2 functions, • jDENP,MM (CEC 2011: RWIC) – LNCS SIDE 2012, • SPSRDEMMS (CEC 2013: RPSOO); Large-scale @SWEVO. • DISH (SWEVO 2019) – best CEC 2015 & 2017 results. • Performance assessment of the algorithms at world EA championships: several times best on some criteria (also won CEC 2009 dynamic optimization competition). • Performance assessment on several industry challenges • procedural tree models reconstruction (ASOC 2011, INS 2013), • underwater glider path planning (ASOC 2014), • hydro-thermal energy scheduling (APEN 2015), • RWIC (Real World Industry Challenges) - CEC 2011; 2013, ... Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time & Parallel Bench. 13/64
- 14. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix SPSRDEMMS: Example of Optimization Mechanisms • SPSRDEMMS = Structured Population Size Reduction Differential Evolution with Multiple Mutation Strategies • canonical DE, upgraded with: mechanism of F and CR control parameters self-adaptation, mutation strategy ensembles, population structuring (distributed islands), and population size reduction. • is an extension of the jDENP,MM variant (Zamuda and Brest, SIDE 2012) and was published at CEC 2013 (competition). • The SPSRDEMMS, for a ﬁxed part of the population (NPbest number of individuals at end of the entire population), executes only the best/1 strategy. • This part of population (which might be seen as a sub-population) has a separate best vector index, xbest bestpop. • The ﬁrst part of the population (mainpop) operates on target vectors xi ∈ {x1...xNP−NPbest} and second part (bestpop) operates on target vectors xi = {xNP−NPbest+1...xNP}. • Both strategies generate mutation vectors using all vectors of the population x1...xNP, i.e. r1, r2, r3 ∈ {1..NP}. Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time & Parallel Bench. 14/64
- 15. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix Self-adaptive control parameters’ randomization frequency and propagations in differential evolution – Methods • G. Karafotias, M. Hoogendoorn, A. Eiben, Parameter control in evolutionary algorithms: trends and challenges, IEEE Trans. Evolut. Comput. 19 (2) (2015) 167–187. • A. Zamuda, J. Brest, E. Mezura-Montes, Structured population size reduction differential evolution with multiple mutation strategies on CEC 2013 real parameter optimization, in: Proceedings of the 2013 IEEE Congress on Evolutionary Computation (CEC), vol. 1, 2013, pp. 1925–1931. • J. Brest, S. Greiner, B. Bošković, M. Mernik, V. Žumer, Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems, IEEE Trans. Evolut. Comput. 10 (6) (2006) 646–657. • Parameter control study • Systematic approach to answering questions about the control parameters mechanism • For certain interesting functions, deeper insight is shown Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time & Parallel Bench. 15/64
- 16. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix Other Enhancements / Improvements / Mechanisms in DE DE, jDE, SaDE, ODE, DEGL, JADE, EPSDE; ϵ-DE, DDE, CDE; PDE, GDE, DEMO, MOEA/D, ... • Swagatam Das and Ponnuthurai Nagaratnam Suganthan. ”Differential evolution: a survey of the state-of-the-art.” IEEE Transactions on Evolutionary Computation 15(1), 2011: 4-31. DOI: 10.1109/TEVC.2010.2059031. CoDE, Compact DE, L-SHADE, Binary DE, Successful-Parent-Selecting Framework DE, ... • Swagatam Das, Sankha Subhra Mullick, and Ponnuthurai Nagaratnam Suganthan. ”Recent Advances in Differential Evolution – An Updated Survey.” Swarm and Evolutionary Computation, Volume 27, April 2016, Pages 1-30, 2016. DOI: 10.1016/j.swevo.2016.01.004. Several hybridizations, improvements, and general mechanisms. Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time & Parallel Bench. 16/64
- 17. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix Functions of the Problems in 100-Digit Challenge • The stated goal of the 100-Digit Challenge benchmark is: • to understand better “the behavior of swarm and evolutionary algorithms as single objective optimizers” (explainable AI) • Continuous multi-dimensional (D) numerical functions, f(x) • Solution quality is measured in number of precise digits (max. 10 per function) • 10 digits added up per 10 functions = score of 100 No. Problem name X∗ D Search Range 1 Storn’s Chebyshev Polynomial Fitting Problem 1 9 [-8192,8192] 2 Inverse Hilbert Matrix Problem 1 16 [-16384,16384] 3 Lennard-Jones Minimum Energy Cluster 1 18 [-4,4] 4 Rastrigin’s Function 1 10 [-100,100] 5 Griewangk’s Function 1 10 [-100,100] 6 Weierstrass Function 1 10 [-100,100] 7 Modiﬁed Schwefel’s Function 1 10 [-100,100] 8 Expanded Schaffer’s F6 Function 1 10 [-100,100] 9 Happy Cat Function 1 10 [-100,100] 10 Ackley Function 1 10 [-100,100] X∗ denotes an optimum (transformed to 1 for all functions). Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time & Parallel Bench. 17/64
- 18. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix The 17th International Symposium on Operations Research in Slovenia (SOR ’23) 20th – 22nd September 2023, Hotel Astoria, Bled, Slovenia Friday, September 22, 2023, at 11.30 in Hall I: Special Session 6, Industry & Society 5.0: Optimization and Learning in Human and Industrial Environments Organized by the Slovenian Society Informatika, Section of Operations Research Solving 100-Digit Challenge with Score 100 by Extended Running Time and Parallel Benchmarking — II. Method: DISHchain3e+12 Algorithm — Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time & Parallel Bench. 18/64
- 19. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix DISH - a Population-based Optimizer at SWEVO (Q1) Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time & Parallel Bench. 19/64
- 20. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix DISH – Algorithm Deﬁnition (Pseudocode, Parameters) • DISH in C++ code, • published in SWEVO (served BM), • mow applied for 100-digit challenge, • benchmarked using HPC (SLING). • Historical memory size H = 5, • archive size A = NP, • initial population size NP0 = 25 √ D log D and • minimum population size NPmin = 4, • for pBest mutation p = 0.25 and pmin = pmax/2, • with initialization of all but one memory values at MF = 0.5 and MCR = 0.8 and • the one memory entry with MF = MCR = 0.9, and • pBest-w strategy with weight value limits Fw at 0.7F, 0.8F, and 1.2F for Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time & Parallel Bench. 20/64
- 21. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix DISH – Algorithm Mechanisms Detailed xj,i = U h lowerj, upperj i ; ∀j = 1, . . . , D; ∀i = 1, . . . , NP, (1) MCR,i = MF,i = 0.5; ∀i = 1, . . . , H, (2) vi = xr1 + F (xr2 − xr3) , (3) vi = xi + Fi xpbest − xi + Fi (xr1 − xr2) , (4) Fi = C MF,r, 0.1 , (5) uj,i = vj,i if U [0, 1] ≤ CRi or j = jrand xj,i otherwise . (6) CRi = N h MCR,r, 0.1 i . (7) xi,G+1 = ( ui,G if f ui,G ≤ f xi,G xi,G otherwise , (8) MF,k = meanWL (SF) if SF ̸= ∅ MF,k otherwise , (9) MCR,k = meanWL (SCR) if SCR ̸= ∅ MCR,k otherwise , (10) meanWL (S) = P|S| k=1 wk • S2 k P|S| k=1 wk • Sk (11) wk = abs f uk,G − f xk,G P|SCR| m=1 abs f um,G − f xm,G . (12) NPnew = round NPinit − FES MAXFES ∗ (NPinit − NPf) , (13) p = pmin + FES MAXFES (pmax − pmin). (14) vi = xi + Fw(xpBest − xi) + F(xr1 − xr2), (15) Fw = 0.7F, FES 0.2MAXFES, 0.8F, FES 0.4MAXFES, 1.2F, otherwise. (16) wk = r PD j=1 uk,j,G − xk,j,G 2 P|SCR| m=1 r PD j=1 um,j,G − xm,j,G 2 . (17) Colors: • black – L-SHADE base, • gray – overloaded, • blue – new w/ DISH. Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time Parallel Bench. 21/64
- 22. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix The 17th International Symposium on Operations Research in Slovenia (SOR ’23) 20th – 22nd September 2023, Hotel Astoria, Bled, Slovenia Friday, September 22, 2023, at 11.30 in Hall I: Special Session 6, Industry Society 5.0: Optimization and Learning in Human and Industrial Environments Organized by the Slovenian Society Informatika, Section of Operations Research Solving 100-Digit Challenge with Score 100 by Extended Running Time and Parallel Benchmarking — III. Results – Scores, Comparison, Impact — Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time Parallel Bench. 22/64
- 23. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix Tuned parameter values for DISHchain3e+12 algorithm Function MAX FES NP0 1 1e+5 25 √ D log D 2 1e+6 25 √ D log D 3 1e+7 25 √ D log D 4 1e+8 250 √ D log D 5 1e+6 25 √ D log D 6 1e+5 25 √ D log D 7 1e+8 2500 √ D log D 8 1e+11 10000 √ D log D 9 3e+12 25 √ D log D 10 1e+7 25 √ D log D • MAX FES: the maximum function evaluations allowed • Function 9 required the most MAX FES to solve • For functions 4, 7, and 8, larger population NP0 used Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time Parallel Bench. 23/64
- 24. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix Observed Problem Difﬁculty Function evaluations to reach accuracy up to certain digit 10000 1x106 1x108 1x1010 1x1012 0 1 2 3 4 5 6 7 8 9 FES Digits f1 f2 f3 f4 f5 f6 f7 f8 f9 f10 • combined on all functions 1–10, accuracy evolution plot • using logscale axis for FES (function evaluations) Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time Parallel Bench. 24/64
- 25. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix Score Obtained: 100 Fifty runs for each function sorted by the number of correct digits (for DISHchain3e+12 algorithm) Num. correct digits No. Problem name X∗ D Search Range 0 1 2 3 4 5 6 7 8 9 10 Score 1 Storn’s Chebyshev Polynomial Fitting Problem 1 9 [-8192,8192] 0 0 0 0 0 0 0 0 0 0 50 10 2 Inverse Hilbert Matrix Problem 1 16 [-16384,16384] 0 0 0 0 0 0 0 0 0 0 50 10 3 Lennard-Jones Minimum Energy Cluster 1 18 [-4,4] 0 0 0 0 0 0 0 0 0 0 50 10 4 Rastrigin’s Function 1 10 [-100,100] 0 0 0 0 0 0 0 0 0 0 50 10 5 Griewangk’s Function 1 10 [-100,100] 0 0 0 0 0 0 0 0 0 0 50 10 6 Weierstrass Function 1 10 [-100,100] 0 0 0 0 0 0 0 0 0 0 50 10 7 Modiﬁed Schwefel’s Function 1 10 [-100,100] 0 0 0 0 0 0 0 0 0 0 50 10 8 Expanded Schaffer’s F6 Function 1 10 [-100,100] 0 0 0 0 0 0 0 0 0 0 50 10 9 Happy Cat Function 1 10 [-100,100] 0 0 0 0 0 3 5 1 6 1 34 10 10 Ackley Function 1 10 [-100,100] 0 0 0 0 0 0 0 0 0 0 50 10 Score (total):) 100 X∗ denotes an optimum (transformed to 1 for all functions). Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time Parallel Bench. 25/64
- 26. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix Impact: Comparing Score to Other Entries – Rank 1 https://github.com/P-N-Suganthan/CEC2019/blob/master/100-DigitChallengeAnalysisofResults.pdf Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time Parallel Bench. 26/64
- 27. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix The 17th International Symposium on Operations Research in Slovenia (SOR ’23) 20th – 22nd September 2023, Hotel Astoria, Bled, Slovenia Friday, September 22, 2023, at 11.30 in Hall I: Special Session 6, Industry Society 5.0: Optimization and Learning in Human and Industrial Environments Organized by the Slovenian Society Informatika, Section of Operations Research Solving 100-Digit Challenge with Score 100 by Extended Running Time and Parallel Benchmarking — IV: Conclusion with Takeaways — Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time Parallel Bench. 27/64
- 28. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix Conclusion with Takeaways Conclusion: score of 100 (rank 1) Takeaways: 100-digit Challenge; EAs; HPC a key element Thanks! Acknowledgement: this work is supported by ARRS programme P2-0041; and DAPHNE, funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 957407. 10000 1x106 1x108 1x1010 1x1012 0 1 2 3 4 5 6 7 8 9 FES Digits f1 f2 f3 f4 f5 f6 f7 f8 f9 f10 0 1 2 3 4 5 6 7 8 9 Combined power from 110 MW to 975 MW, step 0.01 MW#104 0 100 200 300 400 500 600 700 Individual output (power [MW] or unit total cost [$]) Cost, TC / 3 Powerplant P1 power Powerplant P2 power Powerplant P3 power 150 200 250 300 350 400 450 500 550 16 32 48 64 80 Seconds to compute a workload Number of tasks (equals 16 times the SLURM --nodes parameter) Summarizer workload Real examples: science and HPC Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time Parallel Bench. 28/64
- 29. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix The 17th International Symposium on Operations Research in Slovenia (SOR ’23) 20th – 22nd September 2023, Hotel Astoria, Bled, Slovenia Friday, September 22, 2023, at 11.30 in Hall I: Special Session 6, Industry Society 5.0: Optimization and Learning in Human and Industrial Environments Organized by the Slovenian Society Informatika, Section of Operations Research Solving 100-Digit Challenge with Score 100 by Extended Running Time and Parallel Benchmarking — Final Slide: Questions, Misc Acknowledgement: this work is supported by ARRS programme P2-0041 and EU project no. 957407 (DAPHNE). Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time Parallel Bench. 29/64
- 30. Introduction Backgrounds DIFFERENTIAL EVOLUTION Method Results Conclusion Appendix The 17th International Symposium on Operations Research in Slovenia (SOR ’23) 20th – 22nd September 2023, Hotel Astoria, Bled, Slovenia Friday, September 22, 2023, at 11.30 in Hall I: Special Session 6, Industry Society 5.0: Optimization and Learning in Human and Industrial Environments Organized by the Slovenian Society Informatika, Section of Operations Research Solving 100-Digit Challenge with Score 100 by Extended Running Time and Parallel Benchmarking — Appendix with Marketing Materials — Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time Parallel Bench. 30/64
- 31. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE References Appendix (Vega supercomputer in TOP500) — A Multimedia Tour — Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time Parallel Bench. 31/64
- 32. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE References TOP500: EuroHPC Vega (tour at ASHPC23) Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time Parallel Bench. 32/64
- 33. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE References TOP500: EuroHPC Vega (tour at ASHPC23) Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time Parallel Bench. 33/64
- 34. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE References TOP500: EuroHPC Vega (tour at ASHPC23) Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time Parallel Bench. 34/64
- 35. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE References TOP500: EuroHPC Vega (tour at ASHPC23) Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time Parallel Bench. 35/64
- 36. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE References AI Challenges Shortlist (Part II: First subpart) Faced 5 types of challenges, leading to the needs to apply HPC architectures for benchmarking state-of-the-art topics in 1 text summarization, 2 forest ecosystem modeling, simulation, and visualization, 3 underwater robotic mission planning, 4 energy production scheduling for hydro-thermal power plants, and 5 understanding evolutionary algorithms. Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time Parallel Bench. 36/64
- 37. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE References Challenges 1: Text Summarization (Language) For NLP (Natural Language Processing), part of ”Big Data”. Terms across sentences are determined using a semantic analysis using both: • coreference resolution (using WordNet) and • a Concept Matrix (from Freeling). INPUT CORPUS NATURAL LANGUAGE PROCESSING ANALYSIS CONCEPTS DISTRIBUTION PER SENTENCES MATRIX OF CONCEPTS PROCESSING CORPUS PREPROCESSING PHASE OPTIMIZATION TASKS EXECUTION PHASE ASSEMBLE TASK DESCRIPTION SUBMIT TASKS TO PARALLEL EXECUTION OPTIMIZER + TASK DATA ROUGE EVALUATION The detailed new method called CaBiSDETS is developed in the HPC approach comprising of: • a version of evolutionary algorithm (Differential Evolution, DE), • self-adaptation, binarization, constraint adjusting, and some more pre-computation, • optimizing the inputs to deﬁne the summarization optimization model. Aleš Zamuda, Elena Lloret. Optimizing Data-Driven Models for Summarization as Parallel Tasks. Journal of Computational Science, 2020, vol. 42, pp. 101101. DOI: 10.1016/j.jocs.2020.101101 Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time Parallel Bench. 37/64
- 38. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE References Challenges 2: Forest Ecosystem Modeling, Simulation, and Visualization (Real World / Video) • HPC need to process spatial data and add procedural content, generating real-world items for producing a video of 3D space. Videos: https://www.youtube.com/watch?list=PL7pmTW8neV7tZf2qx1wV5zbD74sUyHL3Bv=V9YJgYO_sIA Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time Parallel Bench. 38/64
- 39. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE References Challenges 3: Underwater Robotic Mission Planning • Computational Fluid Dynamics (CFD) spatio-temporal model of the ocean currents for autonomous vehicle navigation path planning. • Constrained Differential Evolution Optimization for Underwater Glider Path Planning in Sub-mesoscale Eddy Sampling. • Corridor-constrained optimization: eddy border region sampling — new challenge for UGPP DE. • Feasible path area is constrained — trajectory in corridor around the border of an ocean eddy. The objective of the glider here is to sample the oceanographic variables more efﬁciently, while keeping a bounded trajectory. HPC: develop new methods and evaluate them. Video: https://www.youtube.com/watch?v=4kCsXAehAmU Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time Parallel Bench. 39/64
- 40. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE References Challenges 4: Energy Production Scheduling for Hydro-thermal Power Plants A. Glotić, A. Zamuda. Short-term combined economic and emission hydrothermal optimization by surrogate differential evolution. Applied Energy, 1 March 2015, vol. 141, pp. 42-56. DOI: 10.1016/j.apenergy.2014.12.020 Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time Parallel Bench. 40/64
- 41. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE References Challenges 5: Understanding Evolutionary Algorithms • Evolutionary algorithms benchmarking to understand computational intelligence of these algorithms (→ storage requirement!), • aim: Machine Learning to design an optimization algorithm (learning to learn). • Example CI Algorithm Mechanism Design: Control Parameters Self-Adaptation (in DE). Video: https://www.youtube.com/watch?v=R244LZpZSG0 Application stacks for real code: inspired by previous computational optimization competitions in continuous settings that used test functions for optimization application domains: • single-objective: CEC 2005, 2013, 2014, 2015 • constrained: CEC 2006, CEC 2007, CEC 2010 • multi-modal: CEC 2010, SWEVO 2016 • black-box (target value): BBOB 2009, COCO 2016 • noisy optimization: BBOB 2009 • large-scale: CEC 2008, CEC 2010 • dynamic: CEC 2009, CEC 2014 • real-world: CEC 2011 • computationally expensive: CEC 2013, CEC 2015 • learning-based: CEC 2015 • 100-digit (50% targets): 2019 joined CEC, SEMCCO, GECCO • multi-objective: CEC 2002, CEC 2007, CEC 2009, CEC 2014 • bi-objective: CEC 2008 • many objective: CEC 2018 Tuning/ranking/hyperheuristics use. → DEs as usual winner algorithms. Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time Parallel Bench. 41/64
- 42. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE References Challenges 6: new DAPHNE Use Cases Example Use Cases • DLR Earth Observation • ESA Sentinel-1/2 datasets 4PB/year • Training of local climate zone classifiers on So2Sat LCZ42 (15 experts, 400K instances, 10 labels each, ~55GB HDF5) • ML pipeline: preprocessing, ResNet-20, climate models • IFAT Semiconductor Ion Beam Tuning • KAI Semiconductor Material Degradation • AVL Vehicle Development Process (ejector geometries, KPIs) • ML-assisted simulations, data cleaning, augmentation • Cleaning during exploratory query processing [So2Sat LC42: https://mediatum.ub.tum.de/1454690] [Xiao Xiang Zhu et al: So2Sat LCZ42: A Benchmark Dataset for the Classification of Global Local Climate Zones. GRSM 8(3) 2020] Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time Parallel Bench. 42/64
- 43. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE References HPC Initiatives (Part II: Second subpart) Timeline (as member) of recent impactful HPC initiatives including Slovenia: • SLING: Slovenian national supercomputing network, 2010-05-03–, • SIHPC: Slovenian High-Performance Computing Centre, 2016-03-04– • ImAppNIO: Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice, 2016-03-09–2020-10-31 • cHiPSet: High-Performance Modelling and Simulation for Big Data Applications, 2015-04-08–2019-04-07, • HPC RIVR: UPGRADING OF NATIONAL RESEARCH INFRASTRUCTURES, Investment Program, 2018-03-01–2020-09-15, • TFoB: IEEE CIS Task Force on Benchmarking, January 2020–, • EuroCC: National Competence Centres in the framework of EuroHPC, 2020-09-01–(2022-08-31), • DAPHNE: Integrated Data Analysis Pipelines for Large-Scale Data Management, HPC, and Machine Learning, 2020-12-01–(2024-11-30). Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time Parallel Bench. 43/64
- 44. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE References Initiatives: SLING, SIHPC, HPC RIVR, EuroCC • There is a federated and orchestrated aim towards HPC infrastructure in Slovenia, especially through: • SLING: Slovenian national supercomputing network → has federated the initiative push towards orchestration of HPC resources across the country. • SIHPC: Slovenian High-Performance Computing Centre → has orchestrated the ﬁrst EU funds application towards HPC Teaming in the country (and Participation of Slovenia in PRACE 2). • HPC RIVR: UPGRADING OF NATIONAL RESEARCH INFRASTRUCTURES, Investment Program → has provided an investment in experimental HPC infrastructure. • EuroCC: National Competence Centres in the framework of EuroHPC → has secured a National Competence Centre (EuroHPC). Vega supercomputer online Consortium Slovenian High-Performance Computing Centre Aškerčeva ulica 6 SI-1000 Ljubljana Slovenia Ljubljana, 22. 2. 2017 prof. dr. Anwar Osseyran PRACE Council Chair Subject: Participation of Slovenia in PRACE 2 Dear professor Osseyran, In March 2016 a consortium Slovenski superračunalniški center (Slovenian High-Performance Computing Centre - SIHPC) was established with a founding act (Attachment 1) where article 6 claims that the consortium will join PRACE and that University of Ljubljana, Faculty of mechanical engineering (ULFME) will represent the consortium in the PRACE. The legal representative of ULFME in the consortium and therefore, also in the PRACE (i.e., the delegate in PRACE Council with full authorization) is prof. dr. Jožef Duhovnik, as follows from appointment of the dean of ULFME (Attachment 2). The consortium has also agreed that it joins PRACE 2 optional programme as contributing GP. With best regards, assist. prof. dr. Aleš Zamuda, vice-president of SIHPC Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time Parallel Bench. 44/64
- 45. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE References Initiatives: ImAppNIO, cHiPSet, TFoB, DAPHNE Aim towards software to run HPC and improve capabilities: • ImAppNIO: Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice, → improve capabilities through benchmarking (to understand (and to learn to learn)) CI algorithms • cHiPSet: High-Performance Modelling and Simulation for Big Data Applications, → include HPC in Modelling and Simulation (of the process to be learned) • TFoB: IEEE CIS Task Force on Benchmarking, → includes CI benchmarking opportunities, where HPC would enable new capabilities. • DAPHNE: Integrated Data Analysis Pipelines for Large-Scale Data Management, HPC, and Machine Learning. → to deﬁne and build an open and extensible system infrastructure for integrated data analysis pipelines, including data management and processing, high-performance computing (HPC), and machine learning (ML) training and scoring https://daphne-eu.github.io/ Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time Parallel Bench. 45/64
- 46. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE References EuroHPC Vega Deploying DAPHNE (Part II: Third subpart) MODA (Monitoring and Operational Data Analytics) tools for • collecting, analyzing, and visualizing • rich system and application data, and • my opinion on how one can make sense of the data for actionable insights. • Explained through previous examples: from a HPC User Perspective. Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time Parallel Bench. 46/64
- 47. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE References MODA Actionable Insights, Explained From a HPC User Perspective, Through the Example of Summarization Most interesting ﬁndings of summarization on HPC example are • the ﬁtness of the NLP model keeps increasing with prolonging the dedicated HPC resources (see below) and that • the ﬁtness improvement correlates with ROUGE evaluation in the benchmark, i.e. better summaries. -0.05 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 1 10 100 1000 10000 ROUGE-1R ROUGE-2R ROUGE-LR ROUGE-SU4R Fitness (scaled) Hence, the use of HPC signiﬁcantly contributes to capability of this NLP challenge. However, the MODA insight also provided the useful task running times and resource usage. Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time Parallel Bench. 47/64
- 48. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE References Running the Tasks on HPC: ARC Job Preparation Parallel summarization tasks on grid through ARC. Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time Parallel Bench. 48/64
- 49. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE References Running the Tasks on HPC: ARC Job Submission, Results Retrieval Merging [JoCS2020] Through an HPC approach and by parallelization of tasks, a data-driven summarization model optimization yields improved benchmark metric results (drawn using gnuplot merge). MODA is needed to run again and improve upon, to forecast how to set required task running time and resources (predicting system response). Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time Parallel Bench. 49/64
- 50. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE References Monitoring and Operational Data Analytics • Monitor used (jobs, CPU/wall time, etc.): Smirnova, Oxana. The Grid Monitor. Usage manual, Tech. Rep. NORDUGRID-MANUAL-5, The NorduGrid Collaboration, 2003. http://www.nordugrid.org/documents/ http://www.nordugrid.org/manuals.html http://www.nordugrid.org/documents/monitor.pdf • Deployed at: www.nordugrid.org/monitor/ • NorduGrid Grid Monitor Sampled: 2021-06-28 at 17-57-08 • Nation-wide in Slovenia: https://www.sling.si/gridmonitor/loadmon.php http://www.nordugrid.org/monitor/index.php? display=vo=Slovenia Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time Parallel Bench. 50/64
- 51. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE References MODA Example From: ARC at Jost Example experiments from DOI: 10.1016/j.swevo.2015.10.007 (SPSRDEMMS) – job YYULDmGOXpmnmmR0Xox1SiGmABFKDmABFKDmrtMKDmABFKDm66faPo. Sample ARC ﬁle gridlog/diag (2–3 day Wall Times). runtimeenvironments=APPS/ARNES/MPI−1.6−R ; CPUUsage=99% MaxResidentMemory=5824kB AverageUnsharedMemory=0kB AverageUnsharedStack=0kB AverageSharedMemory=0kB PageSize=4096B MajorPageFaults=4 MinorPageFaults=1213758 Swaps=0 ForcedSwitches=36371494 WaitSwitches=170435 Inputs=45608 Outputs=477168 SocketReceived=0 SocketSent=0 Signals =0 nodename=wn003 . arnes . s i WallTime=148332s Processors=16 UserTime=147921.14s KernelTime =2.54 s AverageTotalMemory=0kB AverageResidentMemory=0kB LRMSStartTime=20150906104626Z LRMSEndTime=20150908035838Z exitcode=0 Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time Parallel Bench. 51/64
- 52. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE References EuroCC HPC: Vega (TOP500 #106, HPCG #56 — June 2021) • Researchers apply to EuroHPC JU calls for access. • Regular calls opened in 2021 fall (Benchmark Development). • https://prace-ri.eu/benchmark-and-development-access-information-for-applicants/ • 60% capacities for national share (70% OA, 20% commercial, 10% host (community, urgent priority of national importance, maintenance)) + 35% EuroHPC JU share (approved applications) • Has a SLURM dev partition for SSH login (SLURM partitions w/ CPUs: login[0001-0004]=128; login[0005-0008]=64; cn[0001-384,0577-0960]=256; cn[0385-0576]=256; gn[01-60]=256). Listing 1: Setting up at Vega — slurm dev partition access (login). 1 [ ales . zamuda@vglogin0007 ˜]$ s i n g u l a r i t y pull qmake . s i f docker : / / ak352 /qmake−opencv 2 [ ales . zamuda@vglogin0007 ˜]$ s i n g u l a r i t y run qmake . s i f bash 3 cd sum; qmake ; make clean ; make 4 5 [ ales . zamuda@vglogin0007 ˜]$ cat runme . sh 6 # ! / bin / bash 7 cd sum time mpirun 8 − −mca btl openib warn no device params found 0 9 . / summarizer 10 − −useBinaryDEMPI − −i n p u t f i l e mRNA−1273−t x t 11 − −withoutStatementMarkersInput 12 − −printPreprocessProgress calcInverseTermFrequencyndTermWeights 13 − −printOptimizationBestInGeneration 14 − −summarylength 600 − −NP 200 15 − −GMAX 400 16 summarizer . out . $SLURM PROCID 17 2 summarizer . err . $SLURM PROCID Text summarization/generation systems are getting more and more useful and accessible on deployed systems (e.g. OpenAI’s ChatGPT, Microsoft’s Bing AI part, NVIDIA’s (Fin)Megatron, BLOOM, LaMDA, BERT, VALL-E, Point-E, etc.). -0.65 -0.6 -0.55 -0.5 -0.45 -0.4 -0.35 -0.3 -0.25 -0.2 -0.15 -0.1 1 10 100 Evaluation Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time Parallel Bench. 52/64
- 53. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE References MODA at First EuroCC HPC Vega Supercomputer Listing 2: Runnig at Vega MODA. 1 ===================================================================== GMAX=200 ===== 2 [ ales . zamuda@vglogin0002 ˜]$ srun − −cpu−bind=cores − −nodes=1 − −ntasks−per−node=101 3 − −cpus−per−task=2 − − mem=180G s i n g u l a r i t y run qmake . s i f bash 4 srun : job 4531374 queued and waiting for resources 5 srun : job 4531374 has been allocated resources 6 [ ”$SLURM PROCID” = 0 ] . / runme . sh 7 real 5m22.475 s 8 user 484m42.262 s 9 sys 1m38.304 s 10 ===================================================================== NODES=51 ===== 11 [ ales . zamuda@vglogin0002 ˜]$ srun − −cpu−bind=cores − −nodes=1 − −ntasks−per−node=51 12 − −cpus−per−task=2 − − mem=180G s i n g u l a r i t y run qmake . s i f bash 13 srun : job 4531746 queued and waiting for resources 14 srun : job 4531746 has been allocated resources 15 [ ”$SLURM PROCID” = 0 ] . / runme . sh 16 real 13m57.851 s 17 user 431m25.833 s 18 sys 0m29.272 s 19 ===================================================================== GMAX=400 ===== 20 [ ales . zamuda@vglogin0002 ˜]$ srun − −cpu−bind=cores − −nodes=1 − −ntasks−per−node=101 21 − −cpus−per−task=2 − − mem=180G s i n g u l a r i t y run qmake . s i f bash 22 srun : job 4532697 queued and waiting for resources 23 srun : job 4532697 has been allocated resources 24 [ ”$SLURM PROCID” = 0 ] . / runme . sh 25 real 6m14.687 s 26 user 590m45.641 s 27 sys 1m40.930 s Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time Parallel Bench. 53/64
- 54. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE References More Output: SLURM accounting Listing 3: Example accounting tool at Vega: sacct. [ ales . zamuda@vglogin0002 ˜]$ sacct 4531374. ext+ extern vega−users 202 COMPLETED 0:0 4531746. ext+ extern vega−users 102 COMPLETED 0:0 4532697. ext+ extern vega−users 202 COMPLETED 0:0 [ ales . zamuda@vglogin0002 ˜]$ sacct −j 4531374 −j 4531746 −j 4532697 −o MaxRSS , MaxVMSize , AvePages MaxRSS MaxVMSize AvePages − − − − − − − − − − − − − − − − − − − − − − − − − − − − − − 0 217052K 0 26403828K 1264384K 22 0 217052K 0 13325268K 1264380K 0 0 217052K 0 26404356K 1264384K 30 Future MODA testings: • testing the web interface for job analysis (as available from HPC RIVR); • proﬁling MPI inter-node communication; • use proﬁlers and monitoring tools available — in the context of heterogeneous setups, like e.g. • TAU Performance System — http://www.cs.uoregon.edu/research/tau/home.php, • LIKWID Performance Tools — https://hpc.fau.de/research/tools/likwid/. Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time Parallel Bench. 54/64
- 55. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE References Deploying DAPHNE on Vega Main documentation ﬁle: Deploy.md Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time Parallel Bench. 55/64
- 56. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE References SLURM Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time Parallel Bench. 56/64
- 57. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE References Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time Parallel Bench. 57/64
- 58. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE References Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time Parallel Bench. 58/64
- 59. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE References More HPC User Perspective Nation-wide in Slovenia More: at University of Maribor, Bologna study courses for teaching (training) of Computer Science at cycles — click URL: • level 1 (BSc) • year 1: Programming I – e.g. C++ syntax • year 2: Computer Architectures – e.g. assembly, microcode, ILP • year 3: Parallell and Distributed Computing – e.g. OpenMP, MPI, CUDA • level 2 (MSc) • year 1: Cloud Computing Deployment and Management – e.g. arc, slurm, Hadoop, containers (docker, singularity) through virtualization • level 3 (PhD) • EU and other national projects research: HPC RIVR, EuroCC, DAPHNE, ... – e.g. scaling new systems of CI Operational Research of ... over HPC • IEEE Computational Intelligence Task Force on Benchmarking • Scientiﬁc Journals (e.g. SWEVO, TEVC, JoCS, ASOC, INS) These contribute towards Sustainable Development of HPC. Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time Parallel Bench. 59/64
- 60. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE References Biography and References: Organizations • Associate Professor at University of Maribor, Slovenia • Continuous research programme funded by Slovenian Research Agency, P2-0041: Computer Systems, Methodologies, and Intelligent Services • EU H2020 Research and Innovation project, holder for UM part: Integrated Data Analysis Pipelines for Large-Scale Data Management, HPC, and Machine Learning (DAPHNE), https://cordis.europa.eu/project/id/957407 • IEEE (Institute of Electrical and Electronics Engineers) SM • IEEE Computational Intelligence Society (CIS), senior member • IEEE CIS Task Force on Benchmarking, chair Website link • IEEE CIS, Slovenia Section Chapter (CH08873), chair • IEEE Slovenia Section, 2018–2021 vice chair, 2018-21 • IEEE Young Professionals Slovenia, 2016-19 chair • ACM SIGEVO (Special Interest Group on Genetic and Evolutionary Computation); EurAI; SLAIS • Associate Editor: Swarm and Evolutionary Computation (2016-’22), Human-centric Computing and Information Sciences, Frontiers in robotics and AI (section Robot Learning and Evolution) • Co-operation in Science and Techology (COST) Association Management Committee, member: • CA COST Action CA15140: Improving Applicability of Nature-Inspired Optimisation by Joining Theory and Practice (ImAppNIO), WG3 VC • ICT COST Action IC1406 High-Performance Modelling and Simulation for Big Data Applications (cHiPSet); • More: SI-HPC vice-chair; HPC-RIVR user; EuroHPC Vega user; SLAIS Honorary Tribunal; SLING KO member; Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time Parallel Bench. 60/64
- 61. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE References Biography and References: Top Publications • Aleš Zamuda, Elena Lloret. Optimizing Data-Driven Models for Summarization as Parallel Tasks. Journal of Computational Science, 2020, vol. 42, pp. 101101. DOI 10.1016/j.jocs.2020.101101. • A. Zamuda, J. D. Hernández Sosa. Success history applied to expert system for underwater glider path planning using differential evolution. Expert Systems with Applications, 2019, vol. 119, pp. 155-170. DOI 10.1016/j.eswa.2018.10.048 • C. Lucas, D. Hernández-Sosa, D. Greiner, A. Zamuda, R. Caldeira. An Approach to Multi-Objective Path Planning Optimization for Underwater Gliders. Sensors, 2019, vol. 19, no. 24, pp. 5506. DOI 10.3390/s19245506. • A. Viktorin, R. Senkerik, M. Pluhacek, T. Kadavy, A. Zamuda. Distance Based Parameter Adaptation for Success-History based Differential Evolution. Swarm and Evolutionary Computation, 2019, vol. 50, pp. 100462. DOI 10.1016/j.swevo.2018.10.013. • A. Zamuda, J. Brest. Self-adaptive control parameters’ randomization frequency and propagations in differential evolution. Swarm and Evolutionary Computation, 2015, vol. 25C, pp. 72-99. DOI 10.1016/j.swevo.2015.10.007. • A. Zamuda, J. D. Hernández Sosa, L. Adler. Constrained Differential Evolution Optimization for Underwater Glider Path Planning in Sub-mesoscale Eddy Sampling. Applied Soft Computing, 2016, vol. 42, pp. 93-118. DOI 10.1016/j.asoc.2016.01.038. • A. Zamuda, J. D. Hernández Sosa. Differential Evolution and Underwater Glider Path Planning Applied to the Short-Term Opportunistic Sampling of Dynamic Mesoscale Ocean Structures. Applied Soft Computing, vol. 24, November 2014, pp. 95-108. DOI 10.1016/j.asoc.2014.06.048. • A. Zamuda, J. Brest. Vectorized Procedural Models for Animated Trees Reconstruction using Differential Evolution. Information Sciences, vol. 278, pp. 1-21, 2014. DOI 10.1016/j.ins.2014.04.037. • A. Zamuda, J. Brest. Environmental Framework to Visualize Emergent Artiﬁcial Forest Ecosystems. Information Sciences, vol. 220, pp. 522-540, 2013. DOI 10.1016/j.ins.2012.07.031. • A. Glotić, A. Zamuda. Short-term combined economic and emission hydrothermal optimization by surrogate differential evolution. Applied Energy, 1 March 2015, vol. 141, pp. 42-56. DOI 10.1016/j.apenergy.2014.12.020. • H. Hamann, Y. Khaluf, J. Botev, M. Divband Soorati, E. Ferrante, O. Kosak, J.-M. Montanier, S. Mostaghim, R. Redpath, J. Timmis, F. Veenstra, M. Wahby and A. Zamuda. Hybrid Societies: Challenges and Perspectives in the Design of Collective Behavior in Self-organizing Systems. Frontiers in Robotics and AI, 2016, vol. 3, no. 14. DOI 10.3389/frobt.2016.00014. • J. Šilc, A. Zamuda. Special Issue on ”Bioinspired Optimization” (guest editors). Informatica - An International Journal of Computing and Informatics, 2015, vol. 39, no. 2, pp. 1-122. Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time Parallel Bench. 61/64
- 62. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE References Biography and References: Bound Speciﬁc to HPC PROJECTS: • DAPHNE: Integrated Data Analysis Pipelines for Large-Scale Data Management, HPC, and Machine Learning • ICT COST Action IC1406 High-Performance Modelling and Simulation for Big Data Applications • SLING: Slovenian national supercomputing network • SI-HPC: Slovenian corsortium for High-Performance Computing • UM HPC-RIVR: Supercomputer at UM, https://www.hpc-rivr.si/ • SmartVillages: Smart digital transformation of villages in the Alpine Space • Interreg Alpine Space, https://www.alpine-space.eu/projects/smartvillages/en/home • Interactive multimedia digital signage (PKP, Adin DS) EDITOR: • Associate Editor in journals: • Swarm and Evolutionary Computation (2016-2022), • Human-centric Computing and Information Sciences (2020-2023), • Frontiers in robotics and AI, section Robot Learning and Evolution (2021-2023), • etc. • Mathematics-MDPI, Special Issue Guest Editor: ”Innovations in High-Performance Computing” • Mathematics-MDPI, Special Issue Guest Editor: ”Evolutionary Algorithms in Engineering Design Optimization” • Journal of advanced engineering and computation (member of editorial board since 2019). Viet Nam: Ton Duc Thang University, 2017-. ISSN 2588-123X. • Cloud Computing and Data Science (Associate Editor, since 2019). Universal Wiser Publisher Pte.Ltd. • D. Gleich, P. Planinšič, A. Zamuda. 2018 25th International Conference on Systems, Signals and Image Processing (IWSSIP). IEEE Xplore, Maribor, 20-22 June 2018. • General Chair: 7-th Joint International Conferences on Swarm, Evolutionary and Memetic Computing Conference (SEMCCO 2019) Fuzzy And Neural Computing Conference (FANCCO 2019), Maribor, Slovenia, EU, 10-12 July 2019; co-editors: Aleš Zamuda, Swagatam Das, Ponnuthurai Nagaratnam Suganthan, Bijaya Ketan Panigrahi. • Organizers member: GECCO 2022, GECCO 2023 Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time Parallel Bench. 62/64
- 63. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE References Biography and References: More Publications on HPC • Aleš Zamuda, Elena Lloret. Optimizing Data-Driven Models for Summarization as Parallel Tasks. Journal of Computational Science, 2020, vol. 42, pp. 101101. DOI 10.1016/j.jocs.2020.101101. • Patrick Damme, Marius Birkenbach, Constantinos Bitsakos, Matthias Boehm, Philippe Bonnet, Florina Ciorba, Mark Dokter, Pawel Dowgiallo, Ahmed Eleliemy, Christian Faerber, Georgios Goumas, Dirk Habich, Niclas Hedam, Marlies Hofer, Wenjun Huang, Kevin Innerebner, Vasileios Karakostas, Roman Kern, Tomaž Kosar, Alexander Krause, Daniel Krems, Andreas Laber, Wolfgang Lehner, Eric Mier, Marcus Paradies, Bernhard Peischl, Gabrielle Poerwawinata, Stratos Psomadakis, Tilmann Rabl, Piotr Ratuszniak, Pedro Silva, Nikolai Skuppin, Andreas Starzacher, Benjamin Steinwender, Ilin Tolovski, Pınar Tözün, Wojciech Ulatowski, Yuanyuan Wang, Izajasz Wrosz, Aleš Zamuda, Ce Zhang, Xiao Xiang Zhu. DAPHNE: An Open and Extensible System Infrastructure for Integrated Data Analysis Pipelines. 12th Conference on Innovative Data Systems Research, CIDR 2022, Chaminade, CA, January 9-12, 2022. • Aleš Zamuda, Vincenzo Crescimanna, Juan C. Burguillo, Joana Matos Dias, Katarzyna Wegrzyn-Wolska, Imen Rached, Horacio González-Vélez, Roman Senkerik, Claudia Pop, Tudor Cioara, Ioan Salomie, Andrea Bracciali. Forecasting Cryptocurrency Value by Sentiment Analysis: An HPC-Oriented Survey of the State-of-the-Art in the Cloud Era. Kołodziej J., González-Vélez H. (eds) High-Performance Modelling and Simulation for Big Data Applications. Lecture Notes in Computer Science, vol 11400, 2019, pp. 325-349. DOI 10.1007/978-3-030-16272-6 12. • Nenad Karolija, Aleš Zamuda. On cloud-supported web-based integrated development environment for programming dataﬂow architectures. MILUTINOVIĆ, Veljko (ur.), KOTLAR, Milos (ur.). Exploring the DataFlow supercomputing paradigm: example algorithms for selected applications, (Computer communications and networks (Internet), ISSN 2197-8433), 2019, pp. 41-51. DOI 10.1007/978-3-030-13803-5 2. • Simone Spolaor, Marco Gribaudo, Mauro Iacono, Tomas Kadavy, Zuzana Komı́nková Oplatková, Giancarlo Mauri, Sabri Pllana, Roman Senkerik, Natalija Stojanovic, Esko Turunen, Adam Viktorin, Salvatore Vitabile, Aleš Zamuda, Marco S. Nobile. Towards Human Cell Simulation. Kołodziej J., González-Vélez H. (eds) High-Performance Modelling and Simulation for Big Data Applications. Lecture Notes in Computer Science, vol 11400, 2019, pp. 221-249. DOI 10.1007/978-3-030-16272-6 8. • A. Zamuda, J. D. Hernandez Sosa, L. Adler. Improving Constrained Glider Trajectories for Ocean Eddy Border Sampling within Extended Mission Planning Time. IEEE Congress on Evolutionary Computation (CEC) 2016, 2016, pp. 1727-1734. • A. Zamuda. Function evaluations upto 1e+12 and large population sizes assessed in distance-based success history differential evolution for 100-digit challenge and numerical optimization scenarios (DISHchain 1e+12): a competition entry for ”100-digit challenge, and four other numerical optimization competitions” at the genetic and evolutionary computation conference (GECCO) 2019. Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO 2019), 2019, pp. 11-12. • ... several more experiments for papers run using HPCs. • ... also, pedagogic materials in Slovenian and English — see Conclusion . Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time Parallel Bench. 63/64
- 64. AI Challenges Shortlist HPC Initiatives EuroHPC Vega ,Deploying DAPHNE References Promo materials: Calls for Papers, Websites CS FERI WWW CIS TFoB CFPs WWW LI Twitter Aleš Zamuda 7@aleszamuda Solving 100-Digit Challenge w/ Score 100 by Extended Running Time Parallel Bench. 64/64