Scientific COBOL: The Road Not Taken
Scientific COBOL: The Road Not Taken
In the annals of computing history, few programming languages have been as distinctly pigeonholed as COBOL (Common Business-Oriented Language). Ask any programmer about COBOL, and they'll immediately think of business applications, financial systems, and transaction processing. Yet there's a fascinating but largely forgotten chapter in COBOL's history: its brief flirtation with scientific computing.
The Scientific Computing Experiment
In the 1960s, as computing was finding its footing, the boundaries between business and scientific applications weren't as rigid as they would later become. The U.S. Department of Defense, which had sponsored COBOL's development, saw potential in expanding COBOL's reach into scientific applications. This led to the creation of the COBOL Scientific Library (CSL) in 1967, an ambitious attempt to bring scientific computing capabilities to the business-oriented language.
Historical Context and Key Figures
The COBOL Scientific Library initiative was launched under the direction of the U.S. Navy's Naval Ordnance Laboratory, with significant input from the CODASYL (Conference on Data Systems Languages) committee. According to surviving documentation in the Computer History Museum archives, the initial CSL specification included 50 scientific subroutines, ranging from basic statistical functions to complex matrix operations.
Dr. Grace Hopper, one of COBOL's primary architects, was initially skeptical of the scientific computing extension, reportedly remarking that "COBOL was never intended to be all things to all people." This sentiment would prove prophetic.
Jean Sammet, another key figure in COBOL's development, wrote in her 1969 book "Programming Languages: History and Fundamentals" that the Scientific Library was "an interesting experiment that demonstrates both the flexibility of COBOL and its limitations."
Why Scientific COBOL Made Sense (On Paper)
Several compelling arguments supported the idea of Scientific COBOL:
Unified Language Dream: Organizations running both business and scientific applications could standardize on a single language, simplifying their software infrastructure.
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Data Handling Strengths: COBOL's robust data handling capabilities could benefit scientific applications dealing with large datasets, such as weather forecasting systems.
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Readability: COBOL's English-like syntax could make scientific algorithms more accessible to non-programmers, a significant advantage in an era when computer access was limited.
Code Comparison: The Tale of Two Languages
To understand why Scientific COBOL faced such challenges, let's look at how a simple scientific calculation - computing a standard deviation - would be expressed in both FORTRAN and COBOL using the Scientific Library:
! FORTRAN implementation (circa 1960s)
DIMENSION X(100)
DOUBLE PRECISION SUM, SUMSQ, SD, MEAN
READ (5,10) N, (X(I), I=1,N)
10 FORMAT(I3/(F10.4))
SUM = 0.0
SUMSQ = 0.0
DO 20 I=1,N
SUM = SUM + X(I)
SUMSQ = SUMSQ + X(I)**2
20 CONTINUE
MEAN = SUM/N
SD = SQRT((SUMSQ - SUM**2/N)/(N-1))
WRITE (6,30) SD
30 FORMAT(' Standard Deviation = ',F10.4)
END
IDENTIFICATION DIVISION.
PROGRAM-ID. CALC-STD-DEV.
ENVIRONMENT DIVISION.
DATA DIVISION.
WORKING-STORAGE SECTION.
01 SAMPLE-DATA.
05 NUMBERS OCCURS 100 TIMES
USAGE IS COMPUTATIONAL-2.
01 N PIC 999.
01 I PIC 999.
01 SUM-X USAGE COMPUTATIONAL-2.
01 SUM-SQUARES USAGE COMPUTATIONAL-2.
01 MEAN USAGE COMPUTATIONAL-2.
01 VARIANCE USAGE COMPUTATIONAL-2.
01 STD-DEV USAGE COMPUTATIONAL-2.
PROCEDURE DIVISION.
ACCEPT N.
PERFORM VARYING I FROM 1 BY 1 UNTIL I > N
ACCEPT NUMBERS(I)
ADD NUMBERS(I) TO SUM-X
COMPUTE SUM-SQUARES = SUM-SQUARES +
(NUMBERS(I) ** 2)
END-PERFORM.
COMPUTE MEAN = SUM-X / N.
COMPUTE VARIANCE =
(SUM-SQUARES - ((SUM-X ** 2) / N)) / (N - 1).
COMPUTE STD-DEV = FUNCTION SQRT(VARIANCE).
DISPLAY "Standard Deviation = " STD-DEV.
STOP RUN.
The contrast is stark. While both programs accomplish the same task, the FORTRAN version is more concise and mathematically intuitive. The COBOL version, even with the Scientific Library extensions, requires more verbose declarations and lacks the natural mathematical expression that made FORTRAN appealing to scientists.
The Reality Check
Despite these theoretical advantages, Scientific COBOL faced several insurmountable challenges:
Performance Gap: FORTRAN's optimization for mathematical operations gave it a significant performance advantage that COBOL couldn't match.
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Cultural Divide: The scientific computing community had already invested heavily in FORTRAN, and COBOL's business-oriented syntax felt alien to scientists and engineers.
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Language Design: COBOL's fundamental design choices, optimized for business logic and decimal arithmetic, made it awkward for expressing complex mathematical operations.
Real-World Hybrid Approaches
While pure Scientific COBOL didn't gain widespread adoption (usage surveys from 1971 showed it present in less than 2% of scientific computing installations), some organizations successfully implemented hybrid approaches. Here's a simplified example of how weather data processing might have looked in such a system:
IDENTIFICATION DIVISION.
PROGRAM-ID. WEATHER-DATA-PROCESSOR.
ENVIRONMENT DIVISION.
INPUT-OUTPUT SECTION.
FILE-CONTROL.
SELECT WEATHER-DATA ASSIGN TO "READINGS.DAT"
ORGANIZATION IS LINE SEQUENTIAL.
DATA DIVISION.
FILE SECTION.
FD WEATHER-DATA.
01 WEATHER-RECORD.
05 STATION-ID PIC X(5).
05 TEMPERATURE PIC S999V99.
05 PRESSURE PIC 9999V99.
05 HUMIDITY PIC 999V99.
WORKING-STORAGE SECTION.
01 EOF-FLAG PIC X VALUE "N".
PROCEDURE DIVISION.
OPEN INPUT WEATHER-DATA
PERFORM UNTIL EOF-FLAG = "Y"
READ WEATHER-DATA
AT END
MOVE "Y" TO EOF-FLAG
NOT AT END
CALL "FORTRAN-FORECAST" USING
TEMPERATURE, PRESSURE, HUMIDITY
END-READ
END-PERFORM.
CLOSE WEATHER-DATA.
STOP RUN.
This example shows how COBOL's strength in data handling could complement FORTRAN's mathematical prowess, with COBOL managing data I/O and record processing while delegating the actual weather modeling calculations to FORTRAN subroutines.
Legacy and Lessons
The Scientific COBOL experiment offers valuable insights into programming language evolution:
- The importance of community and ecosystem in a language's success
- How early design decisions can shape a language's trajectory
- The trade-offs between specialization and generalization in programming language design
Modern Parallels
Today's programming landscape shows interesting parallels to this historical division. While we have powerful general-purpose languages, we still see specialization: R and Julia for scientific computing, SQL for data management, Python straddling multiple domains. The ghost of the scientific/business computing divide lives on, albeit in more nuanced forms.
Conclusion
The story of Scientific COBOL serves as a reminder that the path of technological evolution isn't always obvious or inevitable. While COBOL's scientific computing ambitions weren't realized, they represent a fascinating "what-if" in computing history. As we continue to debate the merits of specialized versus general-purpose programming languages, the lessons from this historical experiment remain relevant.
In an era where we're seeing increasing convergence between business and scientific computing through fields like data science and machine learning, perhaps it's worth reflecting on those early attempts to bridge these domains. The story of Scientific COBOL might just have some lessons for our current technological crossroads.
Image: Lucas Jackson from Pixabay
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