Roman Atachiants

Supervised by Dr. Gavin Doherty

Collaborators Dr. David Gregg, Dr. Bérenger Arnaud

Project Title MANYCORE: Understanding Software

Performance on Many-Core Systems

Colour

Photo

6 x 4 cm

Visualising Data Locality Performance

In order to take advantage of the multi-core and many-core

hardware of today and tomorrow, programmers are faced with a

need to parallelize the code to distribute work across multiple CPUs.

User Evaluation

To evaluate the tool,

we have conducted an

experiment with a total

of 33 participants from

industry and academia.

To analyze the results

of the experiment, we

adopted a hybrid quali-

tative and quantitative

approach.

Fieldwork, Taxonomy, Modelling and Validation

While the visualisation itself is being the final step of this research, it

is based on the significant amount of analytical work and an

observational model we’ve created and validated to bridge the gap

The process of paralle-

lization is very complex

and in order to assist

programmers in identi-

fying the performance

of parallel programs re-

lated to poor data loca-

lity, we have designed

an interactive visualis-

ation tool.

3-Step Visualisation

Summary View

Timeline View

Threads View

Our Publications

R. Atachiants, D. Gregg and

G. Doherty. Design

Considerations for

Parallel Performance

Tools. ACM SIG-CHI’14

R. Atachiants, D. Gregg and

G. Doherty. 2015. An

Observational Model for

Identifying Parallel

Performance Problems.

Journal paper submitted and

under revision

R. Atachiants, D. Gregg and

G. Doherty. Visualising

Data Locality Performance

for Parallel Programming.

Submitted for revision.

R. Atachiants. Ph.D.

Thesis: Supporting Visual

Diagnosis of Performance

Problems in Multi-Threaded

Software. [Draft]

Some Results

The participants' correctness in data locality problem identifica-

tion has significantly increased when they used the visualisation.

We received a significant amount of feedback which suggests

that the visualization effectively supports programmers and

reduced the cognitive load of performance problem diagnosis.

Programmers with less than 10

years of experience in the field

rated their diagnosis answers

with significantly more confi-

dence when they used our

visualisation.

between the events and

counters we can collect

and the actual parallel

performance problems.

We have also created a

taxonomy of problems

comprised of 23 parallel

problems and ran two

experiments with the

help of 81 programmers

to validate the taxono-

my and our model.