We implement teraheap in openjdk and evaluate it with 15 widely used applications in two real world big data frameworks, spark and giraph. our evaluation shows that for the same dram size, teraheap improves performance by up to 73% and 28% compared to native spark and giraph, respectively. In this paper, we propose teraheap, a system that eliminates s d overhead and expensive gc scans for a large portion of the objects in big data frameworks. teraheap relies on three concepts. (1) it eliminates s d cost by extending the managed runtime (jvm) to use a second high capacity heap (h2) over a fast storage device.
Teraheap relies on three concepts. (1) it eliminates s d cost by extending the managed runtime (jvm) to use a second high capacity heap (h2) over a fast storage device. (2) it offers a simple hint based interface, allowing big data analytics frameworks to leverage knowledge about objects to populate h2. (3) it reduces gc cost by fencing the. Asplos'23: the 28th international conference on architectural support for programming languages and operating systemssession 4b: memory management near data. Teraheap extends the managed runtime (jvm) to use a second, high capacity heap over a fast storage device that coexists with the regular heap. teraheap provides direct access to objects on the second heap (no s d). it also reduces gc cost by fencing the garbage collector from scanning the second heap. teraheap leverages frameworks’ property. Teraheap: reducing memory pressure in managed big data frameworks iacovos g. kolokasis , giannos evdorou , shoaib akram , christos kozanitis , anastasios papagiannis , foivos s. zakkak , polyvios pratikakis , angelos bilas.
Teraheap extends the managed runtime (jvm) to use a second, high capacity heap over a fast storage device that coexists with the regular heap. teraheap provides direct access to objects on the second heap (no s d). it also reduces gc cost by fencing the garbage collector from scanning the second heap. teraheap leverages frameworks’ property. Teraheap: reducing memory pressure in managed big data frameworks iacovos g. kolokasis , giannos evdorou , shoaib akram , christos kozanitis , anastasios papagiannis , foivos s. zakkak , polyvios pratikakis , angelos bilas. Teraheap: reducing memor y pressure in managed big data frameworks iacovos g. kolokasis [email protected] th.gr giannos evdorou [email protected] th.gr foivos zakkak fzakkak@redhat christos kozanitis [email protected] th.gr shoaib akram [email protected] polyvios pratikakis [email protected] th.gr angelos bilas [email protected] th.gr. Teraheap is proposed, a system that eliminates s d overhead and expensive gc scans for a large portion of the objects in big data frameworks and outperforms panthera, a state of the art garbage collector for hybrid memories, by up to 69%. big data analytics frameworks, such as spark and giraph, need to process and cache massive amounts of data that do not always fit on the managed heap.
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