  We present a heterogeneous search algorithm for broadband extended
gravitational-wave emission (BEGE), expected from gamma-ray bursts and
energetic core-collapse supernovae. It searches the $(f,\dot{f})$-plane for
long duration bursts by inner engines slowly exhausting their energy reservoir
by matched filtering on a {\em Graphics Processor Unit} (GPU) over a template
bank of millions of one-second duration chirps. Parseval's Theorem is used to
predict the standard deviation $\sigma$ of filter output, taking advantage of
near-Gaussian noise in LIGO S6 data over 350-2000 Hz. Tails exceeding a
mulitple of $\sigma$ are communicated back to a {\em Central Processing Unit}
(CPU). This algorithm attains about 65\% efficiency overall, normalized to the
Fast Fourier Transform (FFT). At about one million correlations per second over
data segments of 16 s duration $(N=2^{16}$ samples), better than real-time
analysis is achieved on a cluster of about a dozen GPUs. We demonstrate its
application to the capture of high frequency hardware LIGO injections. This
algorithm serves as a starting point for deep all-sky searches in both archive
data and real-time analysis in current observational runs.
