Introduction

  • Following Bruno Latour, thinking of ENIAC as an “actor” in history: “Was ENIAC an essential agent in the construction of the modern world, like cod, salt, or the Irish?” (2)
  • “The atomic bomb depended on recent advances in theoretical and experimental physics, but could be built only thanks to the labor of many thousands of people within specially constructed industrial cities. Such a commitment of effort would have been unlikely in peacetime” (4).
  • ENIAC was particularly important as a test-bed for algorithmically driven simulation, a fundamentally new approach to modeling. The Monte Carlo simulations carried out from 1948 to 1950 on behalf of Los Alamos were landmarks in the history of scientific practice as well as in the history of computer programming. The historian of physics Peter Galison made ENIAC’s role in the development of computer simulation famous, but in chapters 8 and 9 we provide the rst clear and in-depth exploration of exactly what the simulations did and, drawing on the dissertation work of Anne Fitzpatrick, illuminate the contributions they made to the progress of the atomic weapons program” (5).
  • “As with Peter Galison’s earlier discussion of ENIAC’s role in early nuclear simulation, these narratives shift our understanding of ENIAC away from its traditional portrayal as one link in a chain running from primitive to modern computers and toward its work as an instrument for the creation of new kinds of scientific practice. This parallels developments in the humanities outside existing work on history of computing, particularly attempts to establish fields such as ‘platform studies,’ ‘critical code studies,’ and ‘software studies.’’ The challenge is to re-surface from the sea of technical details clutching treasures that justify the dive” (14).

Chapter 8: ENIAC Goes to Monte Carlo

  • This chapter decribes the process by which ENIAC was reprogrammed to facilitate Monte Carlo calculations. From a timeline perspective, remember that all of these calculations came after Trinity.
  • Citations to also explore re: the role of simulations in the history of computing and STS:
    • Mahoney, “Software as Science—Science as Software” in Mapping the History of Computing, Springer, 2002; Hashagen, “The Computation of Nature,” in The Nature of Computation, Springer, 2013; Record, Knowing Instruments, unpublished PhD diss from U of Toronto, 2012. This last one Haigh et al. note explores “the philosophical status of early Monte Carlo simulation.”
  • Galison’s main concept in “Computing Simulations and the Trading Zone”: the computer centered a “common activity” wherein a “heterogeneous community” “traded” experises around this object. Monte Carlo calculations “‘ushered physics into a place paradoxically dislocated from the traditional reality that borrowed from both experimental and theoretical domains’ by building within computers ‘an artificial world in which “experiments” (their term) could take place’” (175).
  • The Monte Carlo simulation “allows us to observe the rst full revolution of what would later be thought of as the software development life cycle” (174).
  • So what is the Monte Carlo method? “The defining element is the application of the laws of chance” (175). “The arrival of electronic computers offered an alternative: simulate the progress over time of a series of virtual neutrons representing members of the population released by the bomb’s neutron initiator when conventional explosives compressed the weapon’s core to form a critical mass and trigger its detonation” (175). The need for such simulations, among other things, was materially determined pragmatism: “With the Monte Carlo approach, the explosive yield of various hypothetical weapon designs could be estimated after fewer test detonations, conserving America’s precious stockpiles of weapons-grade uranium and plutonium” (176).
  • ENIAC had to be rebuilt into a “new control mode” that could take into account the new need for raw computing power; the shift from a general to a specialized computng machine (178).
  • “Flow diagrams, in contrast, explicitly showed the splitting and merging of possible paths of control through a computation. They illustrated the behavior of programs written in the modern code paradigm, whose possible execution paths would diverge following conditional transfer instructions. In some situations (for example, when deciding whether a neutron was traveling inward or outward in the assembly) translating the computing plan into a ow diagram was fairly straight-forward. In other cases significant changes to the structure of the original computation were required” (180).
  • The need for random numbers: eventually Von Neumann developed a subroutine to generate pseudo-random numbers within ENIAC itself (184).

Chapter 9: ENIAC Tries Its Luck

  • “Von Neumann used the word ‘expedition’ to refer to these [calculations at Los Alamos], and to later trips made to Aberdeen to experiment with numerical weather predictions. ‘Expedition’ is a striking word, capturing a world in which computers were scare and exotic things by evoking the scientific tradition of mounting long, arduous, and painstakingly planned field expeditions to observe eclipses, uncover buried cities, or explore the polar regions. Explorers returned with knowledge that could never be obtained at home. Using ENIAC was an adventure, a journey to an unfamiliar place, and often something of an ordeal” (193).
  • Three “runs”: the first, a proof-of-concept on nuclear diffusion; the second, “actual weapons configurations under investigation at Los Alamos”; the third,
  • Note the work of the word “actual” in all of these reports written after the MC problems were run.
  • Also note that the MC simulations did not produce immediate answers, but rather increasing amounts of analyzable material in the form of punch cards (198).
  • “The story told here fits, in its broad outline, with Galison’s famous depiction of the first Monte Carlo simulations as a trading zone, yet it looks more closely than Galison did at the details of the computations, and it deepens our understanding of what is being traded and by whom. According to Galison, Monte Carlo simulation led physics to a ‘netherland that was at once nowhere and everywhere.’ That description of Monte Carlo simulation’s intellectual legacy also describes the unconventional social structure of the project, creating shadowy opportunities in unexpected places. Monte Carlo simulation engaged not only the great men who populate Galison’s story, who were from diverse disciplinary backgrounds, but also a broader cast of characters lacking elite scientifoc credentials,” e.g. Klara von Neumann (205).

Chapter 10: ENIAC Settles Down to Work

  • Note the section on “Daily Life with ENIAC” (212–14) on how folks lived with this machine (particularly thinking about air conditioning and humidity) and “The Weather Simulations” (214–20).
  • “Like the Monte Carlo simulations, this work used ENIAC to demonstrate the feasibility of what later would become a hugely important area of scientific practice. In later decades, research centers for weather and climate research, such as the National Center for Atmospheric Research, vied with the atomic research laboratories and their huge Monte Carlo simulations to be the initial and most influential purchasers of each new model of record-breaking supercomputer” (214).

Conclusion

  • Section on “Algorithmic Simulation” (285–86).
  • “The basic shift was from analytical descriptions of a situation, in which an equation explained the relationship between different quantities, to an algorithmic approach in which the relationship was described only by a series of steps necessary to transform input into output. In that sense, simulation is a characteristically digital practice, and the ENIAC Monte Carlo calculations may plausibly be described as the first computerized simulations” (285–86).
  • “Simulation provided a fundamentally experimental way of discovering the properties of the system described. One set initial parameters, ran the program, and waited to see what happened” (286).