MADNESS

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MADNESS
Original author(s)George Fann, Robert J. Harrison
Developer(s)Oak Ridge National Laboratory, Stony Brook University, Virginia Tech, Argonne National Laboratory
Initial releaseForthcoming
Stable release
0.10[1] / 6 July 2015; 8 years ago (6 July 2015)
Repository
GNU GPL v2
Websitegithub.com/m-a-d-n-e-s-s/madness

MADNESS (Multiresolution Adaptive Numerical Environment for Scientific Simulation) is a high-level software environment for the solution of

differential equations in many dimensions using adaptive and fast harmonic analysis methods with guaranteed precision based on multiresolution analysis
[2] [3] and separated representations .[4]

There are three main components to MADNESS. At the lowest level is a

parallel programming
environment [5] that aims to increases programmer productivity and code performance/scalability while maintaining backward compatibility with current programming tools such as the message-passing interface and Global Arrays. The numerical capabilities built upon the parallel tools provide a high-level environment for composing and solving numerical problems in many (1-6+) dimensions. Finally, built upon the numerical tools are new applications with initial focus upon chemistry,[6] [7] , atomic and molecular physics,[8] material science, and nuclear structure. It is
CPUs
is an ongoing research effort. [9] .[10] Adapting the irregular computation in MADNESS to heterogeneous platforms is nontrivial due to the size of the kernel, which is too small to be offloaded via compiler directives (e.g.
GPU
systems .[11] Intel has publicly stated that MADNESS is one of the codes running on the
Intel MIC
architecture [12] [13] but no performance data has been published yet.

MADNESS' chemistry capability includes

Hartree–Fock and density functional theory
in chemistry [14] [15] (including analytic derivatives,[16] response properties [17] and
time-dependent density functional theory
with asymptotically corrected potentials [18]) as well as nuclear density functional theory[19] and Hartree–Fock–Bogoliubov theory. [20][21] MADNESS and
TDDFT
using wavelets .[22] Many-body wavefunctions requiring six-dimensional spatial representations are also implemented (e.g. MP2[23]). The parallel runtime inside of MADNESS has been used to implement a wide variety of features, including graph optimization .[24] From a mathematical perspective, MADNESS emphasizes rigorous numerical precision without loss of computational performance .
partial differential equations
.[26]

MADNESS was recognized by the R&D 100 Awards in 2011.[27][28] It is an important code to Department of Energy supercomputing sites and is being used by both the leadership computing facilities at Argonne National Laboratory[29] and Oak Ridge National Laboratory[30] to evaluate the stability and performance of their latest supercomputers. It has users around the world, including the United States and Japan .[31] MADNESS has been a workhorse code for computational chemistry in the DOE INCITE program [32] at the Oak Ridge Leadership Computing Facility [33] and is noted as one of the important codes to run on the Cray Cascade architecture.[34]

See also

  • List of numerical analysis software
  • List of quantum chemistry and solid state physics software

References

  1. ^ "Release 0.10". 6 July 2015. Retrieved 14 March 2018.
  2. doi:10.1016/j.acha.2011.10.001
    .
  3. S2CID 7385463
    .
  4. doi:10.1016/j.acha.2007.01.001
    .
  5. ^ Thornton, W. Scott; Vence, Nicholas; Harrison, Robert E. (2009). "Introducing the MADNESS numerical framework for petascale computing" (PDF). Proceedings of the Cray User Group Conference.
  6. doi:10.1016/j.cplett.2013.01.065
    .
  7. doi:10.1016/j.comptc.2013.05.006
    .
  8. doi:10.1103/PhysRevA.85.033403
    .
  9. S2CID 17880870
    .
  10. ^ Shin, Jaewook; Hall, Mary W.; Chame, Jacqueline; Chen, Chun; Hovland, Paul D. (2009). "Autotuning and specialization: Speeding up matrix multiply for small matrices with compiler technology" (PDF). Proceedings of the Fourth International Workshop on Automatic Performance Tuning.[permanent dead link]
  11. S2CID 5637880
    .
  12. ^ James Reinders (20 September 2012). "Intel Xeon Phi coprocessor support by software tools".
  13. ^ Timothy Prickett Morgan (16 November 2011). "Hot Intel teraflops MIC coprocessor action in a hotel". The Register.
  14. PMID 15634124. Archived from the original
    on 2013-02-23. Retrieved 2019-05-15.
  15. PMID 15473723. Archived from the original
    on 2013-02-24. Retrieved 2019-05-15.
  16. PMID 15291596. Archived from the original
    on 2013-02-23. Retrieved 2019-05-15.
  17. PMID 18647020. Archived from the original
    on 2013-02-23. Retrieved 2019-05-15.
  18. S2CID 96910088
    .
  19. ^ "UNEDF SciDAC Collaboration Universal Nuclear Energy Density Functional". Archived from the original on 2013-04-03. Retrieved 2012-11-19.
  20. S2CID 119215739
    .
  21. S2CID 119281109
    .
  22. S2CID 96589229
    .
  23. PMID 22979846. Archived from the original
    on 2013-02-23. Retrieved 2019-05-15.
  24. doi:10.2172/1042920
    .
  25. ^ Harrison, Robert J.; Fann, George I. (2007). "SPEED and PRECISION in QUANTUM CHEMISTRY". SciDAC Review. 1 (3): 54–65. Archived from the original on 2012-08-03. Retrieved 2012-11-19.
  26. doi:10.1016/j.cpc.2011.07.001
    .
  27. R&D Magazine
    . 14 August 2011. Retrieved November 26, 2012.
  28. ^ "MADNESS Named R&D 100 Winner".
  29. ^ "Accurate Numerical Simulations Of Chemical Phenomena Involved in Energy Production and Storage with MADNESS and MPQC".
  30. ^ "Application Readiness at ORNL" (PDF).
  31. ^ "Far from home - Japanese graduate student journeys to UT to study computational chemistry". Archived from the original on 2012-12-15.
  32. ^ "Chemistry and Materials Simulations Speed Clean Energy Production and Storage". 1 June 2011. Archived from the original on 6 August 2011.
  33. ^ Bland, A.; Kendall, R.; Kothe, D.; Rogers, J.; Shipman, G. (2010). "Jaguar: The world's most powerful computer" (PDF). Proceedings of the Cray User Group Conference. Archived from the original (PDF) on 2012-12-24.
  34. ^ "Cray unveils 100 petaflop XC30 supercomputer". 8 November 2012.

External links

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