1. The LEGaTO project has received funding from the European Union's Horizon 2020 research and innovation programme under
the grant agreement No 780681
9 September 2020
Infection Research with
Maxeler Dataflow
Computing
LEGaTO Final Event: Low-Energy Heterogeneous
Computing Workshop
Tobias Becker
Maxeler Technologies

2. Low-Energy Heterogeneous Computing Workshop
Introduction – Biomarkers Analysis
• Biomarkers can serve many unique purposes including :
− screening for early signs of disease, or monitoring of progression
− monitoring effects of the treatments
− study organ function
• Various data types
− measurable molecules found in body tissues, cells and fluids
− MRI
− function tests
− heart rate, blood pressure
• Modern high-throughout technologies can produce lots of data:
micro array, next-gen sequencing, mass spectroscopy
• Goal: Using statistical methods to research effectiveness of drugs,
vaccination strategies and harmfulness of pathogens
2 09/09/20

3. Low-Energy Heterogeneous Computing Workshop
Computational aspects
• High-throughput data from biological experiments are gathered into vectors
whose dimension correspond to the number of measurements
• For statistical analysis, the number of samples (n) must be larger than the
number of biomarker candidates (p) for good classification performance to get a
reliable diagnosis.
• Obtaining samples is expensive
• Basis: Pilot studies
− Small sample size but large number of candidates (short fat data)
− Distinguishing real correlations from random correlations
• Strategy:
− Select top features from thousands that can predict the cases
− Estimating the appropriate sample size to get significant results
3 09/09/20

4. Low-Energy Heterogeneous Computing Workshop
Algorithm: Biomarker Candidate Evaluation
4 09/09/20
Pilot study with small sample size
Global evaluation of single biomarkers
More ``good´´
single
biomarkers
than expceted?
Discard/Redesign study
no
Compute
potential of
biomarker
combinations
& estimate
required
sample size to
validate
combinations
yes
Real Data
Compute goodness
of BCs
(Entropy)
Random Data
Evaluate pilot study
(HiPerMAb)
Estimate p-values
Maxeler DFE
Implemented in R
Time consuming: runs hours on typical datasets

5. Low-Energy Heterogeneous Computing Workshop
Optimisation
• Goals:
− Decrease simulation runtime
− Enable to handle larger data sets and more
complex problems
− Decrease energy consumption
• Target:
− Maxeler dataflow computer with MAX5 DFE
− Also works on Xilinx Alveo and Amazon EC2 F1
• Optimisation plan:
− Manual: Port R code to C and MaxJ
− Optimise using LEGaTO toolflow: Port to OmpSs
and MaxJ
5

6. Low-Energy Heterogeneous Computing Workshop
PCI
Express
Manager
DFE
Memory
MyManager (.maxj)
x
x
+
30
x
Manager m = new Manager();
Kernel k =
new MyKernel();
m.setKernel(k);
m.setIO(
link(“x", CPU),
m.build();
link(“y", CPU));
MyKernel(DATA_SIZE,
x, DATA_SIZE*4,
y, DATA_SIZE*4);
for (int i =0; i < DATA_SIZE; i++)
y[i]= x[i] * x[i] + 30;
Main
Memory
CPU
CPU
Code
Host Code (.c)
MaxCompiler: Application Development in MaxJ
6
SLiC
MaxelerOS
DFEVar x = io.input("x", dfeInt(32));
DFEVar result = x * x + 30;
io.output("y", result, dfeInt(32));
MyKernel (.maxj)
int*x, *y;
x
x
+
30
y

7. Low-Energy Heterogeneous Computing Workshop
Practical Acceleration Process in MaxJ
Focus profiling on loop structure, not function calls
init_run : 66.92s CPU 66.92s WALL ( 1 calls)
electrons : 3116.79s CPU 3116.79s WALL ( 1 calls)
Called by electrons:
v_of_rho : 17.91s CPU 17.91s WALL ( 51 calls)
c_bands : 2305.58s CPU 2305.58s WALL ( 50 calls)
sum_band : 597.16s CPU 597.16s WALL ( 50 calls)
mix_rho : 2.80s CPU 2.80s WALL ( 50 calls)
Called by c_bands:
h_psi : 962.63s CPU 962.63s WALL ( 684 calls)
g_psi : 25.24s CPU 25.24s WALL ( 582 calls)
cdiaghg : 666.02s CPU 666.02s WALL ( 682 calls)
Called by h_psi:
add_vuspsi : 79.00s CPU 79.00s WALL ( 684 calls)
General routines
calbec : 56.10s CPU 56.10s WALL ( 684 calls)
fft : 3.30s CPU 3.30s WALL ( 560 calls)
fftw : 1156.55s CPU 1156.50s WALL ( 97578 calls)

8. Low-Energy Heterogeneous Computing Workshop
Task-based kernel identification for DFE mapping
• OmpSs identifies “static” task graphs while running
• Annotation of IO and compute help to create DFE task model
• Instantiate static, customized, ultra-deep (>1,000 stages) computing pipelines

9. Low-Energy Heterogeneous Computing Workshop
LEGaTO tools for DFE mapping
9 September 2020
task graph
loopflow graph
• automatically generate loopflow graph
• annotate with key information

10. Low-Energy Heterogeneous Computing Workshop
Results
10
• Application accelerated with Maxeler DFE
− Accelerated Cut Index function (entropy calculation) in Biomarker Evaluation
− Added hardware random number generator to Cut Index accelerator
− Evaluated 10^6 problem size on Jülich testbed
− Validated new toolflow and results
Original R code C++ Cut Index DFE Cut Index
Time[s] 4514 74.8 5.49
Speedup 1(baseline) 60 822
Original R code C++ Cut Index DFE Cut Index
Energy [Wh] 575 9.7 1.3
efficiency gain 1(baseline) 59 443

11. Thanks!