1,963 research outputs found
ILP-based approaches to partitioning recurrent workloads upon heterogeneous multiprocessors
The problem of partitioning systems of independent constrained-deadline sporadic tasks upon heterogeneous multiprocessor platforms is considered. Several different integer linear program (ILP) formulations of this problem, offering different tradeoffs between effectiveness (as quantified by speedup bound) and running time efficiency, are presented
Provably good task assignment on heterogeneous multiprocessor platforms for a restricted case but with a stronger adversary
Consider the problem of scheduling a set of
implicit-deadline sporadic tasks to meet all deadlines on
a heterogeneous multiprocessor platform. We consider a
restricted case where the maximum utilization of any task on
any processor in the system is no greater than one. We use
an algorithm proposed in [1] (we refer to it as LP-EE) from
state-of-the-art for assigning tasks to heterogeneous multiprocessor
platform and (re-)prove its performance guarantee
for this restricted case but for a stronger adversary. We show
that if a task set can be scheduled to meet deadlines on a
heterogeneous multiprocessor platform by an optimal task
assignment scheme that allows task migrations then LP-EE
meets deadlines as well with no migrations if given processors
twice as fast
A conjecture about provably good task assignment on heterogeneous multiprocessor platforms but with a stronger adversary
Consider the problem of scheduling a set of
implicit-deadline sporadic tasks to meet all deadlines on a
heterogeneous multiprocessor platform. We use an algorithm
proposed in [1] (we refer to it as LP-EE) from state-of-the-art
for assigning tasks to heterogeneous multiprocessor platform
and (re-)prove its performance guarantee but for a stronger
adversary.We conjecture that if a task set can be scheduled to
meet deadlines on a heterogeneous multiprocessor platform
by an optimal task assignment scheme that allows task
migrations then LP-EE meets deadlines as well with no
migrations if given processors twice as fast. We illustrate
this with an example
Provably good scheduling of sporadic tasks with resource sharing on a two-type heterogeneous multiprocessor platform
Consider the problem of scheduling a set of implicit-deadline sporadic tasks to meet all deadlines on a two-type
heterogeneous multiprocessor platform where a task may request at most one of |R| shared resources. There are m1
processors of type-1 and m2 processors of type-2. Tasks may migrate only when requesting or releasing resources. We
present a new algorithm, FF-3C-vpr, which offers a guarantee that if a task set is schedulable to meet deadlines by an
optimal task assignment scheme that only allows tasks to migrate when requesting or releasing a resource, then FF-3Cvpr
also meets deadlines if given processors 4+6*ceil(|R|/min(m1,m2)) times as fast. As far as we know, it is the first
result for resource sharing on heterogeneous platforms with provable performance
Heterogeneous Computing on Mixed Unstructured Grids with PyFR
PyFR is an open-source high-order accurate computational fluid dynamics
solver for mixed unstructured grids that can target a range of hardware
platforms from a single codebase. In this paper we demonstrate the ability of
PyFR to perform high-order accurate unsteady simulations of flow on mixed
unstructured grids using heterogeneous multi-node hardware. Specifically, after
benchmarking single-node performance for various platforms, PyFR v0.2.2 is used
to undertake simulations of unsteady flow over a circular cylinder at Reynolds
number 3 900 using a mixed unstructured grid of prismatic and tetrahedral
elements on a desktop workstation containing an Intel Xeon E5-2697 v2 CPU, an
NVIDIA Tesla K40c GPU, and an AMD FirePro W9100 GPU. Both the performance and
accuracy of PyFR are assessed. PyFR v0.2.2 is freely available under a 3-Clause
New Style BSD license (see www.pyfr.org).Comment: 21 pages, 9 figures, 6 table
Intra-type migrative scheduling of implicit-deadline sporadic tasks on two- type heterogeneous multiprocessor
Consider the problem of scheduling a set of implicit-deadline sporadic tasks to meet all deadlines on a two-type
heterogeneous multiprocessor platform. Each processor is either of type-1 or type-2 with each task having different
execution time on each processor type. Jobs can migrate between processors of same type (referred to as intra-type
migration) but cannot migrate between processors of different types. We present a new scheduling algorithm namely,
LP-Relax(THR) which offers a guarantee that if a task set can be scheduled to meet deadlines by an optimal task
assignment scheme that allows intra-type migration then LP-Relax(THR) meets deadlines as well with intra-type
migration if given processors 1/THR as fast (referred to as speed competitive ratio) where THR <= 2/3
- …