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PROJECT 06  ·  High-performance computing · Geospatial analysis

Wildfire Density Mapping
A large spatial problem, made parallel

A hybrid Python and C++ OpenMP pipeline for calculating wildfire density from NASA FIRMS fire points over a high-resolution grid.

FIELD NOTES / 06—07
06
OPTIMIZE
Bhanu Teja Malineni06 / 07
RoleProject developer
PeriodProject work
ContextGeospatial data analysis
FocusHigh-performance computing

The idea

This project examined wildfire point data at high spatial resolution. The scale of the density calculation made performance a central engineering challenge.

Approach

I developed a hybrid Python/C++ OpenMP pipeline to process 787,327 NASA FIRMS fire points across a 960,000-pixel grid, involving approximately 755 billion Haversine density calculations. Profiling exposed false sharing as a multi-core bottleneck; after diagnosing it, the reported speedup was 1.35× with four threads.

01PrepareLoad and organize 787,327 NASA FIRMS fire points.
02CalculateCompute Haversine-based density over a 960,000-pixel grid.
03OptimizeDiagnose false sharing and parallelize with OpenMP.

Reported outcomes

787,327NASA FIRMS fire points processed.
960,000Pixels in the analysis grid.
~755BReported Haversine density calculations.
1.35×Reported speedup with four threads after diagnosing false sharing.
Scale and performance figures are reported in the source resume. The speedup is specific to the reported four-thread setup.

Tools & methods

PythonC++OpenMPNASA FIRMSParallel computingHaversine distance
More about the project

The work combined Python for data handling with C++ and OpenMP for computation. The important optimization finding was that shared-memory behavior created false sharing and limited multi-core performance.

What I took from it

This project made the cost of memory behavior visible: adding threads alone did not remove the bottleneck, so profiling and diagnosing false sharing mattered to improving throughput.