Piecewise　signals　appear　in　many　application　fields.　Here,　we　propose　a　framework　for　segmenting　such　signals　based　on　the　modeling　of　each　piece　using　a　parametric　probability　distribution.　The　proposed　framework　first　models　the　segmentation　as　an　optimization　problem　with　sparsity　regularization.　Then,　an　algorithm　based　on　dynamic　programming　is　utilized　for　finding　the　optimal　solution.　However,　dynamic　programming　often　suffers　from　a　heavy　computational　burden.　Therefore,　we　further　show　that　the　proposed　framework　is　parallelizable　and　propose　using　GPU-based　parallel　computing　to　accelerate　the　computation.　This　approach　is　highly　desirable　for　the　analysis　of　large　volumes　of　data　that　are　ubiquitous.　The　experiments　on　both　the　simulated　and　real　genomic　datasets　from　the　next-generation　sequencing　demonstrate　an　improved　performance　in　terms　of　both　segmentation　quality　and　computational　speed.