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Parallel Processing 
A Simple Case Study
Parallel Processing - What is it? 
• Concurrent System 
– Two or more actions progressing in parallel 
– This can be on a single core 
• Parallel System 
– Two or more actions executing in parallel 
– This requires multiple cores 
– Parallel systems are a subset of concurrent systems 
• Distributed System 
– Two or more actions executing in parallel 
– This requires multiple connected machines 
– Communication is primarily via messaging
Why application developers need to 
know? 
• Multi-core processors 
• Large volume of data 
• Analytics and other new fields 
• Cheaper hardware
OS Resource Challenges 
Memory 
(ROM/ 
RAM) 
DB NW NW 
Web 
CPU 
Registers 
Cache 
Service 
Queue 
Distribu 
ted 
Mem
Representation of an Application 
Source Data Transform Data Sink 
- UI 
- DB 
- Queue 
- Network 
- Service 
- UI 
- DB 
- Queue 
- Network 
- Service
Abstraction of Concurrancy 
• Programs are execution of atomic 
statements 
• Concurrent programs are the interleavings 
of atomic statements 
• All possible interleavings should produce 
the same results 
• No process should be excluded from any 
arbitrary interleaving
Challenges 
• Race Conditions 
• Deadlocks
To go Parallel? 
• SpeedUp 
– Amdahl’s Law 
• Speedup <= 1/((1 – PCTpar) + (PCTpar/P)) 
– PCTpar -> percentage of time in parallel 
– P -> Number of cores 
• 75% PCTpar provides 3x speedup on a 8 core 
– Gustafson-Barsis’s Law 
• Speedup <= P + (1 – P) S 
– P -> Number of cores 
– S -> Percentage of time spend in serial code 
• Efficiency 
– Speedup/Cores -> resource utilization as %age
Things to Consider 
• Identify independent computations 
• Implement concurrancy at the highest level 
• Make no assumptions on the cores 
• Use the best processing model 
• Never assume a particular execution order 
• User thread local storage or associate locks 
to specific data 
• Make no assumptions on the order of 
execution
Methedology 
• Start with a tuned and functional serial code 
• Analysis: identify possible concurrancy 
– Identify hotspots probably using profilers 
• Calculate speedup and efficiency 
• Design and implementation 
• Test for correctness 
– Loop executions, rounding errors 
• Tune for performance 
– Enough work load to compensate for overheads 
• Measure Speedup as a multiplier (e.g. 2x faster) 
– Serial code elapsed time/parallel code elapsed time
Sample Process State Diagram 
Shared 
Variable
Highlights of Process Development 
• Single threaded functional code (Java) 
• Threads spawned off for each message 
– Data decomposition 
– Task decomposition 
– Concurrency at the highest level 
• Monitor (synchronize) & conditional variable 
• Code to ignore deadlocks 
• Tested and tuned to remove bottlenecks 
• Scalable for any number of cores
Monitor 
public class Monitor { 
private static int count = 0; 
private static int max = 0; 
public boolean isSemaphoreUsed() 
{ 
return (count < max); 
} 
public Semaphore(int num) { 
count = num; 
max = num; 
} 
private synchronized void 
incCount() { 
count++; 
} 
private synchronized void 
decCount() { 
count--; 
} 
public synchronized void acquire() 
{ 
try { 
while (count == 0) { 
} 
decCount(); 
} 
public synchronized void release() 
{ 
incCount(); 
} 
} 
Never comes 
out
Monitor with Conditional Variables 
public class Monitor { 
private static int count = 0; 
private static int max = 0; 
public boolean isSemaphoreUsed() 
{ 
return (count < max); 
} 
public Semaphore(int num) { 
count = num; 
max = num; 
} 
private synchronized void 
incCount() { 
count++; 
} 
private synchronized void 
decCount() { 
count--; 
} 
public synchronized void acquire() 
{ 
try { 
while (count == 0) { 
this.wait(); 
} 
decCount(); 
} catch (InterruptedException e) 
{} 
} 
public synchronized void release() 
{ 
incCount(); 
this.notify(); 
} 
}
General Design Guidelines 
• Keep it simple stupid (KISS) 
– Break the problem into simple components 
• Keep the number of layers to minimum 
– Don’t add layers since they are hot in industry 
• Do the right thing 
– Skeptics will provide scenarios to take care
bnair@asquareb.com 
blog.asquareb.com 
https://github.com/bijugs 
@gsbiju

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Concurrency

  • 1. Parallel Processing A Simple Case Study
  • 2. Parallel Processing - What is it? • Concurrent System – Two or more actions progressing in parallel – This can be on a single core • Parallel System – Two or more actions executing in parallel – This requires multiple cores – Parallel systems are a subset of concurrent systems • Distributed System – Two or more actions executing in parallel – This requires multiple connected machines – Communication is primarily via messaging
  • 3. Why application developers need to know? • Multi-core processors • Large volume of data • Analytics and other new fields • Cheaper hardware
  • 4. OS Resource Challenges Memory (ROM/ RAM) DB NW NW Web CPU Registers Cache Service Queue Distribu ted Mem
  • 5. Representation of an Application Source Data Transform Data Sink - UI - DB - Queue - Network - Service - UI - DB - Queue - Network - Service
  • 6. Abstraction of Concurrancy • Programs are execution of atomic statements • Concurrent programs are the interleavings of atomic statements • All possible interleavings should produce the same results • No process should be excluded from any arbitrary interleaving
  • 7. Challenges • Race Conditions • Deadlocks
  • 8. To go Parallel? • SpeedUp – Amdahl’s Law • Speedup <= 1/((1 – PCTpar) + (PCTpar/P)) – PCTpar -> percentage of time in parallel – P -> Number of cores • 75% PCTpar provides 3x speedup on a 8 core – Gustafson-Barsis’s Law • Speedup <= P + (1 – P) S – P -> Number of cores – S -> Percentage of time spend in serial code • Efficiency – Speedup/Cores -> resource utilization as %age
  • 9. Things to Consider • Identify independent computations • Implement concurrancy at the highest level • Make no assumptions on the cores • Use the best processing model • Never assume a particular execution order • User thread local storage or associate locks to specific data • Make no assumptions on the order of execution
  • 10. Methedology • Start with a tuned and functional serial code • Analysis: identify possible concurrancy – Identify hotspots probably using profilers • Calculate speedup and efficiency • Design and implementation • Test for correctness – Loop executions, rounding errors • Tune for performance – Enough work load to compensate for overheads • Measure Speedup as a multiplier (e.g. 2x faster) – Serial code elapsed time/parallel code elapsed time
  • 11. Sample Process State Diagram Shared Variable
  • 12. Highlights of Process Development • Single threaded functional code (Java) • Threads spawned off for each message – Data decomposition – Task decomposition – Concurrency at the highest level • Monitor (synchronize) & conditional variable • Code to ignore deadlocks • Tested and tuned to remove bottlenecks • Scalable for any number of cores
  • 13. Monitor public class Monitor { private static int count = 0; private static int max = 0; public boolean isSemaphoreUsed() { return (count < max); } public Semaphore(int num) { count = num; max = num; } private synchronized void incCount() { count++; } private synchronized void decCount() { count--; } public synchronized void acquire() { try { while (count == 0) { } decCount(); } public synchronized void release() { incCount(); } } Never comes out
  • 14. Monitor with Conditional Variables public class Monitor { private static int count = 0; private static int max = 0; public boolean isSemaphoreUsed() { return (count < max); } public Semaphore(int num) { count = num; max = num; } private synchronized void incCount() { count++; } private synchronized void decCount() { count--; } public synchronized void acquire() { try { while (count == 0) { this.wait(); } decCount(); } catch (InterruptedException e) {} } public synchronized void release() { incCount(); this.notify(); } }
  • 15. General Design Guidelines • Keep it simple stupid (KISS) – Break the problem into simple components • Keep the number of layers to minimum – Don’t add layers since they are hot in industry • Do the right thing – Skeptics will provide scenarios to take care