Project Description
Benchmarks of high performance computer systems are important for a
number of reasons. They allow us to compare the performance of
different systems against one another. They can help guide the design,
development, optimization, and evaluation of scientific applications
running on these systems. Finally, they provide important guidance to
policy-makers and funding agencies in deciding what kind of machines are
suitable for a given set of compute applications.
The High Performance Conjugate Gradient (HPCG) has recently been
proposed as a new benchmark for the evaluation of HPC systems. One of
the central goals of HPCG is to provide a more faithful measure of the
real world performance of an HPC system.
In its reference implementation, the HPCG benchmark is very challenging
for machines that derive a lot of their compute power from hardware
accelerators (GPUs and Intel Xeon Phi) such as Titan. This is because
one of the most expensive computational kernels in HPCG is a triangular
solve. The data dependencies in triangular solves strongly limit the
amount of parallelism available in this kernel and thus they cannot be
accelerated well with GPUs.
By using a new triangular solver library, GELUS, specifically designed
for GPUs, we have been able to significantly accelerate the HPCG
benchmark in our preliminary studies on Titan. Depending
on problem size, our GPU implementation is between three and eight times
faster than a CPU implementation (comparing one GPU against all CPU
cores in a single node on Titan). Importantly, this was achieved by
calling into an independent, off-the-shelf solver library without
tweaking the internals of the library to the specifics of the benchmark
or of the machine.
Allocation History
Source | Hours | Start Date | End Date |
---|---|---|---|
OLCF DIRECTOR'S DISCRETIONARY PROGRAM | 3,000,000 | 2014-06-20 | 2015-06-30 |
OLCF DIRECTOR'S DISCRETIONARY PROGRAM | 3,000,000 | 2014-06-20 | 2015-06-30 |