Task　scheduling　can　improve　the　performance　of　parallel　execution　through　optimizing　the　utilization　of　on-chip　computing　resources,　and　thus　it　has　been　widely　studied.　Most　of　the　previous　work　uses　data　access　locality　to　predict　cache　behaviors　for　task　scheduling,　but　usually　suffering　accuracy　and　computational　time　complexity　issues.　This　paper　proposes　an　efficient　task　assignment　algorithm　to　minimize　the　contention　for　shared　caches　on　multi-core　processors　among　parallel　independent　process　level　tasks.　The　proposed　algorithm　leverages　the　property　of　footprint　to　approximately　estimate　the　locality　parameter　of　parallel　tasks,　choosing　the　best　grouping　of　tasks　with　minimum　locality　value　in　a　quick　way　for　task　assignment.　The　calculation　time　is　therefore　significantly　reduced　and　the　algorithm　complexity　is　O(nlog2n).　Meanwhile,　the　algorithm　accuracy　is　very　high.　On　an　Intel　8　cores　dual-processor　system,　the　experimental　results　show　that　the　task　assignment　algorithm　achieves　over　99%　of　the　actual　optimal　performance　on　average　and　outperforms　the　default　Linux　task　scheduling　method　by　an　average　of　over　5%　for　two　sets　of　different　parallel　tasks.　©　2017　IEEE.