Presentation
TaskGenX: A Hardware-Software Proposal for Accelerating Task Parallelism
Event Type
Research Paper
Heterogeneous Systems
Scheduling
System Software & Runtime Systems
TimeWednesday, June 27th1:45pm - 2:15pm
LocationAnalog 1, 2
DescriptionAs chip multi-processors (CMPs) are becoming
more and more complex, software solutions such as parallel
programming models are attracting a lot of attention. Task-based
parallel programming models offer an appealing approach to
utilize complex CMPs. However, the increasing number of cores
on modern CMPs is pushing research towards the use of fine
grained parallelism. Task-based programming models need to
be able to handle such workloads and offer performance and
scalability. Using specialized hardware for boosting performance
of task-based programming models is a common practice in the
research community.
Our paper makes the observation that task creation becomes
a bottleneck when we execute fine grained parallel applications
with task-based programming models. As the number of cores
increases the time spent generating the tasks of the application
is becoming more critical to the entire execution. To overcome
this issue, we propose TaskGenX. TaskGenX offers a solution
for minimizing task creation overheads and relies both on the
runtime system and a dedicated hardware. On the runtime
system side, TaskGenX decouples the task creation from the
other runtime activities. It then transfers this part of the
runtime to a specialized hardware. We draw the requirements
for this hardware in order to boost execution of highly parallel
applications. From our evaluation using 11 parallel workloads on
both symmetric and asymmetric systems, we obtain performance
improvements up to 15, averaging to 3.1 over the baseline.
more and more complex, software solutions such as parallel
programming models are attracting a lot of attention. Task-based
parallel programming models offer an appealing approach to
utilize complex CMPs. However, the increasing number of cores
on modern CMPs is pushing research towards the use of fine
grained parallelism. Task-based programming models need to
be able to handle such workloads and offer performance and
scalability. Using specialized hardware for boosting performance
of task-based programming models is a common practice in the
research community.
Our paper makes the observation that task creation becomes
a bottleneck when we execute fine grained parallel applications
with task-based programming models. As the number of cores
increases the time spent generating the tasks of the application
is becoming more critical to the entire execution. To overcome
this issue, we propose TaskGenX. TaskGenX offers a solution
for minimizing task creation overheads and relies both on the
runtime system and a dedicated hardware. On the runtime
system side, TaskGenX decouples the task creation from the
other runtime activities. It then transfers this part of the
runtime to a specialized hardware. We draw the requirements
for this hardware in order to boost execution of highly parallel
applications. From our evaluation using 11 parallel workloads on
both symmetric and asymmetric systems, we obtain performance
improvements up to 15, averaging to 3.1 over the baseline.
