De　novo　genome　assembly　reconstructs　the　chromosomes　from　massive　relatively　short　fragmented　reads　and　serves　as　fundamental　for　studying　new　species　where　there　is　no　reference　genome.　Wtdbg2　is　a　de　novo　assembler　for　long　reads　that　is　up　to　hundreds　of　kilobases.　It　is　based　on　fuzzy-Bruijn　graph　(FBG)　and　is　ten　times　faster　than　the　cutting-edge　assemblers　such　as　Canu.　However,　the　performance　of　wtdbg2　still　requires　further　improvement:　1)　it　requires　up　to　terabytes　of　memory　to　compute　the　assembly,　which　is　infeasible　to　run　on　commodity　server;　2)　it　requires　tens　of　hours　for　assembling　on　large　datasets　such　as　genomes　of　homo　sapiens.　To　address　the　above　drawbacks,　we　propose　several　optimization　techniques　for　accelerating　wtdbg2　on　commodity　server,　including　a　memory　autotuning　scheme,　sequence　alignment　optimization　and　intermediate　result　elimination　in　the　output　procedure.　We　compare　the　optimized　wtdbg2　with　the　original　implementation　and　two　cutting-edge　assemblers　on　real-world　datasets.　The　experiment　results　demonstrate　that　optimized　wtdbg2　achieves　maximum　and　average　speedup　of　2.31x　and　1.54x　respectively.　In　addition,　our　proposed　optimization　reduces　the　memory　usage　of　wtdbg2　by　39.5%　without　affecting　the　correctness.