Download as PDF, PPTX
![An
Example
1 class Vec {
2 double x, y;
3 sub(v) {
4 res=new Vec(x-v.x, y-v.y);
5 return res;
6 }
7 }
8 for (i = 0; i < N; i++) {
9 t = in[i+2].sub(a[i-1]);
10 q[i] = t;
11 t = in[i+1].sub(a[i-2]);
12 // use of fields of t
13 }
……
80 t=q[*];
81 // use of fields of t
3](https://image.slidesharecdn.com/icse12-120724183252-phpapp01/75/Uncovering-Performance-Problems-in-Java-Applications-with-Reference-Propagation-Profiling-3-2048.jpg)
![An
Example
1 class Vec {
2 double x, y;
3 sub(v) {
4 res=new Vec(x-v.x, y-v.y);
5 return res;
6 }
7 }
8 for (i = 0; i < N; i++) {
9 t = in[i+2].sub(a[i-1]);
10 q[i] = t;
11 t = in[i+1].sub(a[i-2]);
12 // use of fields of t
13 }
……
80 t=q[*];
81 // use of fields of t
4](https://image.slidesharecdn.com/icse12-120724183252-phpapp01/75/Uncovering-Performance-Problems-in-Java-Applications-with-Reference-Propagation-Profiling-4-2048.jpg)
![An
Example
1 class Vec {
2 double x, y;
3 sub(v) {
4 res=new Vec(x-v.x, y-v.y);
5 return res;
6 }
7 }
8 for (i = 0; i < N; i++) {
9 t = in[i+2].sub(a[i-1]);
10 q[i] = t;
11 t = in[i+1].sub(a[i-2]);
12 // use of fields of t
13 }
……
80 t=q[*];
81 // use of fields of t
5](https://image.slidesharecdn.com/icse12-120724183252-phpapp01/75/Uncovering-Performance-Problems-in-Java-Applications-with-Reference-Propagation-Profiling-5-2048.jpg)
![An
Example
1 class Vec {
2 double x, y;
3 sub(v) {
4 res=new Vec(x-v.x, y-v.y);
5 return res;
6 }
7 }
8 for (i = 0; i < N; i++) {
9 t = in[i+2].sub(a[i-1]);
10 q[i] = t;
11 t = in[i+1].sub(a[i-2]);
12 // use of fields of t
13 }
……
80 t=q[*];
81 // use of fields of t
6](https://image.slidesharecdn.com/icse12-120724183252-phpapp01/75/Uncovering-Performance-Problems-in-Java-Applications-with-Reference-Propagation-Profiling-6-2048.jpg)
![An
Example
1 class Vec {
2 double x, y;
3 sub(v) {
4 res=new Vec(x-v.x, y-v.y);
5 return res;
6 }
7 }
8 for (i = 0; i < N; i++) {
9 t = in[i+2].sub(a[i-1]);
10 q[i] = t;
11 t = in[i+1].sub(a[i-2]);
12 // use of fields of t
13 }
……
80 t=q[*];
81 // use of fields of t
7](https://image.slidesharecdn.com/icse12-120724183252-phpapp01/75/Uncovering-Performance-Problems-in-Java-Applications-with-Reference-Propagation-Profiling-7-2048.jpg)
![An
Example
1 class Vec {
2 double x, y;
3 sub(v) {
4 res=new Vec(x-v.x, y-v.y);
5 return res;
6 }
7 }
8 for (i = 0; i < N; i++) {
9 t = in[i+2].sub(a[i-1]);
10 q[i] = t;
11 t = in[i+1].sub(a[i-2]);
12 // use of fields of t
13 }
……
80 t=q[*];
81 // use of fields of t
8](https://image.slidesharecdn.com/icse12-120724183252-phpapp01/75/Uncovering-Performance-Problems-in-Java-Applications-with-Reference-Propagation-Profiling-8-2048.jpg)
![A
Real
Tuning
Session
1 class Vec {
2 double x, y;
3 sub(v) {
4 res=new Vec(x-v.x, y-v.y);
5 return res;
6 }
7 }
8 for (i = 0; i < N; i++) {
9 t = in[i+2].sub(a[i-1]);
10 q[i] = t;
11 t = in[i+1].sub(a[i-2]);
12 // use of fields of t
13 }
……
80 t=q[*];
81 // use of fields of t
20](https://image.slidesharecdn.com/icse12-120724183252-phpapp01/75/Uncovering-Performance-Problems-in-Java-Applications-with-Reference-Propagation-Profiling-20-2048.jpg)
![A
Real
Tuning
Session
1 class Vec {
2 double x, y;
3 sub(v) {
4 res=new Vec(x-v.x, y-v.y);
5 return res;
6 }
7 }
8 for (i = 0; i < N; i++) {
9 t = in[i+2].sub(a[i-1]);
10 q[i] = t;
11 t = in[i+1].sub(a[i-2]);
12 // use of fields of t
13 }
……
80 t=q[*];
81 // use of fields of t
21](https://image.slidesharecdn.com/icse12-120724183252-phpapp01/75/Uncovering-Performance-Problems-in-Java-Applications-with-Reference-Propagation-Profiling-21-2048.jpg)
![A
Real
Tuning
Session
1 class Vec {
2 double x, y;
1 2
3 sub(v) {
4 res=new Vec(x-v.x, y-v.y);
5 return res;
6 }
7 }
8 for (i = 0; i < N; i++) {
9 t = in[i+2].sub(a[i-1]); 1
10 q[i] = t;
11 t = in[i+1].sub(a[i-2]); 2
12 // use of fields of t
13 }
……
80 t=q[*];
81 // use of fields of t
22](https://image.slidesharecdn.com/icse12-120724183252-phpapp01/75/Uncovering-Performance-Problems-in-Java-Applications-with-Reference-Propagation-Profiling-22-2048.jpg)
![A
Real
Tuning
Session
1 class Vec {
2 double x, y;
1 2
3 sub(v) {
4 res=new Vec(x-v.x, y-v.y);
5 return res;
6 }
7 }
8 for (i = 0; i < N; i++) {
9 t = in[i+2].sub(a[i-1]); 1
10 q[i] = t;
11 t = in[i+1].sub(a[i-2]); 2
12 // use of fields of t
13 }
……
80 t=q[*];
81 // use of fields of t
23](https://image.slidesharecdn.com/icse12-120724183252-phpapp01/75/Uncovering-Performance-Problems-in-Java-Applications-with-Reference-Propagation-Profiling-23-2048.jpg)
![A
Real
Tuning
Session
1 class Vec { 1 class Vec {
2 double x, y; 2 double x, y;
3 sub(v) { 3 sub_rev(v, res) {
4 res=new Vec(x-v.x, y-v.y); 4 res.x = x-v.x;
5 return res; 5 res.y = y-v.y;
6 } 6 }
7 } tuning 7 } = new Vec; // reusable
nt
8 for (i = 0; i < N; i++) { 8 for (i = 0; i < N; i++) {
9 t = in[i+2].sub(a[i-1]); 9 t = in[i+2].sub(a[i-1]);
10 q[i] = t; 10 q[i] = t;
11 t = in[i+1].sub(a[i-2]); 11 in[i+1].sub_rev(a[i-2], nt);
12 // use of fields of t 12 // use of fields of nt
13 } 13 }
…… ……
80 t=q[*]; 80 t=q[*];
81 // use of fields of t 81 // use of fields of t
24](https://image.slidesharecdn.com/icse12-120724183252-phpapp01/75/Uncovering-Performance-Problems-in-Java-Applications-with-Reference-Propagation-Profiling-24-2048.jpg)
![A
Real
Tuning
Session
1 class Vec { 1 class Vec {
2 double x, y; 2 double x, y;
3 sub(v) { 3 sub_rev(v, res) {
4 res=new Vec(x-v.x, y-v.y); 4 res.x = x-v.x;
5 return res; 5 res.y = y-v.y;
6 } 6 }
7 } tuning 7 } = new Vec; // reusable
nt
8 for (i = 0; i < N; i++) { 8 for (i = 0; i < N; i++) {
9 t = in[i+2].sub(a[i-1]); 9 t = in[i+2].sub(a[i-1]);
10 q[i] = t; 10 q[i] = t;
11 t = in[i+1].sub(a[i-2]); 11 in[i+1].sub_rev(a[i-2], nt);
12 // use of fields of t 12 // use of fields of nt
13 } 13 }
…… ……
80 t=q[*]; Reductions: 13% in running time and
80 t=q[*];
81 // use of fields of t 73% in #allocated objectsof fields of t
81 // use
25](https://image.slidesharecdn.com/icse12-120724183252-phpapp01/75/Uncovering-Performance-Problems-in-Java-Applications-with-Reference-Propagation-Profiling-25-2048.jpg)
![An
Example
1 class Vec {
2 double x, y;
3 sub(v) {
4 res=new Vec(x-v.x, y-v.y);
5 return res;
6 }
7 }
8 for (i = 0; i < N; i++) {
9 t = in[i+2].sub(a[i-1]);
10 q[i] = t;
11 t = in[i+1].sub(a[i-2]);
12 // use of fields of t
13 }
……
80 t=q[*];
81 // use of fields of t
3](https://crownmelresort.com/image.slidesharecdn.com/icse12-120724183252-phpapp01/75/Uncovering-Performance-Problems-in-Java-Applications-with-Reference-Propagation-Profiling-3-2048.jpg)
![An
Example
1 class Vec {
2 double x, y;
3 sub(v) {
4 res=new Vec(x-v.x, y-v.y);
5 return res;
6 }
7 }
8 for (i = 0; i < N; i++) {
9 t = in[i+2].sub(a[i-1]);
10 q[i] = t;
11 t = in[i+1].sub(a[i-2]);
12 // use of fields of t
13 }
……
80 t=q[*];
81 // use of fields of t
4](https://crownmelresort.com/image.slidesharecdn.com/icse12-120724183252-phpapp01/75/Uncovering-Performance-Problems-in-Java-Applications-with-Reference-Propagation-Profiling-4-2048.jpg)
![An
Example
1 class Vec {
2 double x, y;
3 sub(v) {
4 res=new Vec(x-v.x, y-v.y);
5 return res;
6 }
7 }
8 for (i = 0; i < N; i++) {
9 t = in[i+2].sub(a[i-1]);
10 q[i] = t;
11 t = in[i+1].sub(a[i-2]);
12 // use of fields of t
13 }
……
80 t=q[*];
81 // use of fields of t
5](https://crownmelresort.com/image.slidesharecdn.com/icse12-120724183252-phpapp01/75/Uncovering-Performance-Problems-in-Java-Applications-with-Reference-Propagation-Profiling-5-2048.jpg)
![An
Example
1 class Vec {
2 double x, y;
3 sub(v) {
4 res=new Vec(x-v.x, y-v.y);
5 return res;
6 }
7 }
8 for (i = 0; i < N; i++) {
9 t = in[i+2].sub(a[i-1]);
10 q[i] = t;
11 t = in[i+1].sub(a[i-2]);
12 // use of fields of t
13 }
……
80 t=q[*];
81 // use of fields of t
6](https://crownmelresort.com/image.slidesharecdn.com/icse12-120724183252-phpapp01/75/Uncovering-Performance-Problems-in-Java-Applications-with-Reference-Propagation-Profiling-6-2048.jpg)
![An
Example
1 class Vec {
2 double x, y;
3 sub(v) {
4 res=new Vec(x-v.x, y-v.y);
5 return res;
6 }
7 }
8 for (i = 0; i < N; i++) {
9 t = in[i+2].sub(a[i-1]);
10 q[i] = t;
11 t = in[i+1].sub(a[i-2]);
12 // use of fields of t
13 }
……
80 t=q[*];
81 // use of fields of t
7](https://crownmelresort.com/image.slidesharecdn.com/icse12-120724183252-phpapp01/75/Uncovering-Performance-Problems-in-Java-Applications-with-Reference-Propagation-Profiling-7-2048.jpg)
![An
Example
1 class Vec {
2 double x, y;
3 sub(v) {
4 res=new Vec(x-v.x, y-v.y);
5 return res;
6 }
7 }
8 for (i = 0; i < N; i++) {
9 t = in[i+2].sub(a[i-1]);
10 q[i] = t;
11 t = in[i+1].sub(a[i-2]);
12 // use of fields of t
13 }
……
80 t=q[*];
81 // use of fields of t
8](https://crownmelresort.com/image.slidesharecdn.com/icse12-120724183252-phpapp01/75/Uncovering-Performance-Problems-in-Java-Applications-with-Reference-Propagation-Profiling-8-2048.jpg)
![A
Real
Tuning
Session
1 class Vec {
2 double x, y;
3 sub(v) {
4 res=new Vec(x-v.x, y-v.y);
5 return res;
6 }
7 }
8 for (i = 0; i < N; i++) {
9 t = in[i+2].sub(a[i-1]);
10 q[i] = t;
11 t = in[i+1].sub(a[i-2]);
12 // use of fields of t
13 }
……
80 t=q[*];
81 // use of fields of t
20](https://crownmelresort.com/image.slidesharecdn.com/icse12-120724183252-phpapp01/75/Uncovering-Performance-Problems-in-Java-Applications-with-Reference-Propagation-Profiling-20-2048.jpg)
![A
Real
Tuning
Session
1 class Vec {
2 double x, y;
3 sub(v) {
4 res=new Vec(x-v.x, y-v.y);
5 return res;
6 }
7 }
8 for (i = 0; i < N; i++) {
9 t = in[i+2].sub(a[i-1]);
10 q[i] = t;
11 t = in[i+1].sub(a[i-2]);
12 // use of fields of t
13 }
……
80 t=q[*];
81 // use of fields of t
21](https://crownmelresort.com/image.slidesharecdn.com/icse12-120724183252-phpapp01/75/Uncovering-Performance-Problems-in-Java-Applications-with-Reference-Propagation-Profiling-21-2048.jpg)
![A
Real
Tuning
Session
1 class Vec {
2 double x, y;
1 2
3 sub(v) {
4 res=new Vec(x-v.x, y-v.y);
5 return res;
6 }
7 }
8 for (i = 0; i < N; i++) {
9 t = in[i+2].sub(a[i-1]); 1
10 q[i] = t;
11 t = in[i+1].sub(a[i-2]); 2
12 // use of fields of t
13 }
……
80 t=q[*];
81 // use of fields of t
22](https://crownmelresort.com/image.slidesharecdn.com/icse12-120724183252-phpapp01/75/Uncovering-Performance-Problems-in-Java-Applications-with-Reference-Propagation-Profiling-22-2048.jpg)
![A
Real
Tuning
Session
1 class Vec {
2 double x, y;
1 2
3 sub(v) {
4 res=new Vec(x-v.x, y-v.y);
5 return res;
6 }
7 }
8 for (i = 0; i < N; i++) {
9 t = in[i+2].sub(a[i-1]); 1
10 q[i] = t;
11 t = in[i+1].sub(a[i-2]); 2
12 // use of fields of t
13 }
……
80 t=q[*];
81 // use of fields of t
23](https://crownmelresort.com/image.slidesharecdn.com/icse12-120724183252-phpapp01/75/Uncovering-Performance-Problems-in-Java-Applications-with-Reference-Propagation-Profiling-23-2048.jpg)
![A
Real
Tuning
Session
1 class Vec { 1 class Vec {
2 double x, y; 2 double x, y;
3 sub(v) { 3 sub_rev(v, res) {
4 res=new Vec(x-v.x, y-v.y); 4 res.x = x-v.x;
5 return res; 5 res.y = y-v.y;
6 } 6 }
7 } tuning 7 } = new Vec; // reusable
nt
8 for (i = 0; i < N; i++) { 8 for (i = 0; i < N; i++) {
9 t = in[i+2].sub(a[i-1]); 9 t = in[i+2].sub(a[i-1]);
10 q[i] = t; 10 q[i] = t;
11 t = in[i+1].sub(a[i-2]); 11 in[i+1].sub_rev(a[i-2], nt);
12 // use of fields of t 12 // use of fields of nt
13 } 13 }
…… ……
80 t=q[*]; 80 t=q[*];
81 // use of fields of t 81 // use of fields of t
24](https://crownmelresort.com/image.slidesharecdn.com/icse12-120724183252-phpapp01/75/Uncovering-Performance-Problems-in-Java-Applications-with-Reference-Propagation-Profiling-24-2048.jpg)
![A
Real
Tuning
Session
1 class Vec { 1 class Vec {
2 double x, y; 2 double x, y;
3 sub(v) { 3 sub_rev(v, res) {
4 res=new Vec(x-v.x, y-v.y); 4 res.x = x-v.x;
5 return res; 5 res.y = y-v.y;
6 } 6 }
7 } tuning 7 } = new Vec; // reusable
nt
8 for (i = 0; i < N; i++) { 8 for (i = 0; i < N; i++) {
9 t = in[i+2].sub(a[i-1]); 9 t = in[i+2].sub(a[i-1]);
10 q[i] = t; 10 q[i] = t;
11 t = in[i+1].sub(a[i-2]); 11 in[i+1].sub_rev(a[i-2], nt);
12 // use of fields of t 12 // use of fields of nt
13 } 13 }
…… ……
80 t=q[*]; Reductions: 13% in running time and
80 t=q[*];
81 // use of fields of t 73% in #allocated objectsof fields of t
81 // use
25](https://crownmelresort.com/image.slidesharecdn.com/icse12-120724183252-phpapp01/75/Uncovering-Performance-Problems-in-Java-Applications-with-Reference-Propagation-Profiling-25-2048.jpg)
Uploaded byDacong (Tony) Yan
Uncovering Performance Problems in Java Applications with Reference Propagation Profiling
The document discusses reference propagation profiling, a technique for uncovering performance problems in Java applications. It is implemented in the Jikes RVM compiler by instrumenting code to track data dependencies and propagate references between memory locations. This allows analyzing applications to find inefficiencies like objects not being assigned to the heap or imbalance between operation costs and benefits. The profiling has high overhead but provides insights to assist with manual performance tuning.
More Related Content
Similar to Uncovering Performance Problems in Java Applications with Reference Propagation Profiling
Uncovering Performance Problems in Java Applications with Reference Propagation Profiling
- 1. Uncovering Performance Problems in Java Applica5ons with Reference Propaga5on Profiling Dacong Yan1, Guoqing Xu2, Atanas Rountev1 1 Ohio State University 2 University of California, Irvine PRESTO: Program Analyses and So5ware Tools Research Group, Ohio State University
- 2. Overview • Performance inefficiencies – O5en exist in Java applicaKons – Excessive memory usage – Long running Kmes, even for simple tasks • Challenges – Limited compiler opKmizaKons – Complicated behavior – Large libraries and frameworks • SoluKon: manual tuning assisted with performance analysis tools 2
- 3. An Example 1 class Vec { 2 double x, y; 3 sub(v) { 4 res=new Vec(x-v.x, y-v.y); 5 return res; 6 } 7 } 8 for (i = 0; i < N; i++) { 9 t = in[i+2].sub(a[i-1]); 10 q[i] = t; 11 t = in[i+1].sub(a[i-2]); 12 // use of fields of t 13 } …… 80 t=q[*]; 81 // use of fields of t 3
- 4. An Example 1 class Vec { 2 double x, y; 3 sub(v) { 4 res=new Vec(x-v.x, y-v.y); 5 return res; 6 } 7 } 8 for (i = 0; i < N; i++) { 9 t = in[i+2].sub(a[i-1]); 10 q[i] = t; 11 t = in[i+1].sub(a[i-2]); 12 // use of fields of t 13 } …… 80 t=q[*]; 81 // use of fields of t 4
- 5. An Example 1 class Vec { 2 double x, y; 3 sub(v) { 4 res=new Vec(x-v.x, y-v.y); 5 return res; 6 } 7 } 8 for (i = 0; i < N; i++) { 9 t = in[i+2].sub(a[i-1]); 10 q[i] = t; 11 t = in[i+1].sub(a[i-2]); 12 // use of fields of t 13 } …… 80 t=q[*]; 81 // use of fields of t 5
- 6. An Example 1 class Vec { 2 double x, y; 3 sub(v) { 4 res=new Vec(x-v.x, y-v.y); 5 return res; 6 } 7 } 8 for (i = 0; i < N; i++) { 9 t = in[i+2].sub(a[i-1]); 10 q[i] = t; 11 t = in[i+1].sub(a[i-2]); 12 // use of fields of t 13 } …… 80 t=q[*]; 81 // use of fields of t 6
- 7. An Example 1 class Vec { 2 double x, y; 3 sub(v) { 4 res=new Vec(x-v.x, y-v.y); 5 return res; 6 } 7 } 8 for (i = 0; i < N; i++) { 9 t = in[i+2].sub(a[i-1]); 10 q[i] = t; 11 t = in[i+1].sub(a[i-2]); 12 // use of fields of t 13 } …… 80 t=q[*]; 81 // use of fields of t 7
- 8. An Example 1 class Vec { 2 double x, y; 3 sub(v) { 4 res=new Vec(x-v.x, y-v.y); 5 return res; 6 } 7 } 8 for (i = 0; i < N; i++) { 9 t = in[i+2].sub(a[i-1]); 10 q[i] = t; 11 t = in[i+1].sub(a[i-2]); 12 // use of fields of t 13 } …… 80 t=q[*]; 81 // use of fields of t 8
- 9. ImplementaKon • Reference propagaKon profiling – Implemented in Jikes RVM 3.1.1 – Modify the runKme compiler for code instrumentaKon – Create shadow loca5ons to track data dependence – Instrument method calls to track interprocedural propaga5on • Overheads – Space: 2-‐3× – Time: 30-‐50× 9
- 10. Reference PropagaKon Profiling • Intraprocedural propagaKon – Shadows for every memory locaKon (stack and heap) to record last assignment that writes to it – Update shadows and the graph accordingly Code Shadow Graph 6 a = new A; aʹ = RefAssign(6,6) 7 b = a; bʹ = RefAssign(6,7) 8 c = new C; cʹ = RefAssign(8, 8) 9 b.fld = c; b.fldʹ = RefAssign(8, 9) 10
- 11. Reference PropagaKon Profiling • Intraprocedural propagaKon – Shadows for every memory locaKon (stack and heap) to record last assignment that writes to it – Update shadows and the graph accordingly Code Shadow Graph 6 a = new A; aʹ = RefAssign(6,6) 7 b = a; bʹ = RefAssign(6,7) 8 c = new C; cʹ = RefAssign(8, 8) 9 b.fld = c; b.fldʹ = RefAssign(8, 9) 11
- 12. Reference PropagaKon Profiling • Intraprocedural propagaKon – Shadows for every memory locaKon (stack and heap) to record last assignment that writes to it – Update shadows and the graph accordingly Code Shadow Graph 6 a = new A; aʹ = RefAssign(6,6) 7 b = a; bʹ = RefAssign(6,7) 8 c = new C; cʹ = RefAssign(8, 8) 9 b.fld = c; b.fldʹ = RefAssign(8, 9) 12
- 13. Reference PropagaKon Profiling • Intraprocedural propagaKon – Shadows for every memory locaKon (stack and heap) to record last assignment that writes to it – Update shadows and the graph accordingly Code Shadow Graph 6 a = new A; aʹ = RefAssign(6,6) 7 b = a; bʹ = RefAssign(6,7) 8 c = new C; cʹ = RefAssign(8, 8) 9 b.fld = c; b.fldʹ = RefAssign(8, 9) 13
- 14. Reference PropagaKon Profiling • Intraprocedural propagaKon – Shadows for every memory locaKon (stack and heap) to record last assignment that writes to it – Update shadows and the graph accordingly Code Shadow Graph 6 a = new A; aʹ = RefAssign(6,6) 7 b = a; bʹ = RefAssign(6,7) 8 c = new C; cʹ = RefAssign(8, 8) 9 b.fld = c; b.fldʹ = RefAssign(8, 9) 14
- 15. Reference PropagaKon Profiling • Intraprocedural propagaKon – Shadows for every memory locaKon (stack and heap) to record last assignment that writes to it – Update shadows and the graph accordingly Code Shadow Graph 6 a = new A; aʹ = RefAssign(6,6) 7 b = a; bʹ = RefAssign(6,7) 8 c = new C; cʹ = RefAssign(8, 8) 9 b.fld = c; b.fldʹ = RefAssign(8, 9) • Interprocedural propagaKon – Per-‐thread scratch space save and restore shadows for 15 parameters and return variables
- 16.
- 17. Client Analyses • Not-‐assigned-‐to-‐heap (NATH) analysis – Locate producer nodes that do not reach heap propagaKon nodes (heap reads and writes) – Variant: mostly-‐NATH analysis 17
- 18. Client Analyses • Not-‐assigned-‐to-‐heap (NATH) analysis – Locate producer nodes that do not reach heap propagaKon nodes (heap reads and writes) – Variant: mostly-‐NATH analysis • Cost-‐benefit imbalance analysis – Detect imbalance between the cost of interesKng operaKons, and the benefits they produce – For example, analysis of write read imbalance 18
- 19. Client Analyses • Not-‐assigned-‐to-‐heap (NATH) analysis – Locate producer nodes that do not reach heap propagaKon nodes (heap reads and writes) – Variant: mostly-‐NATH analysis • Cost-‐benefit imbalance analysis – Detect imbalance between the cost of interesKng operaKons, and the benefits they produce – For example, analysis of write read imbalance • Analysis of never-‐used allocaKons – IdenKfy producer nodes that do not reach the consumer node – Variant: analysis of rarely-‐used allocaKons 19
- 20. A Real Tuning Session 1 class Vec { 2 double x, y; 3 sub(v) { 4 res=new Vec(x-v.x, y-v.y); 5 return res; 6 } 7 } 8 for (i = 0; i < N; i++) { 9 t = in[i+2].sub(a[i-1]); 10 q[i] = t; 11 t = in[i+1].sub(a[i-2]); 12 // use of fields of t 13 } …… 80 t=q[*]; 81 // use of fields of t 20
- 21. A Real Tuning Session 1 class Vec { 2 double x, y; 3 sub(v) { 4 res=new Vec(x-v.x, y-v.y); 5 return res; 6 } 7 } 8 for (i = 0; i < N; i++) { 9 t = in[i+2].sub(a[i-1]); 10 q[i] = t; 11 t = in[i+1].sub(a[i-2]); 12 // use of fields of t 13 } …… 80 t=q[*]; 81 // use of fields of t 21
- 22. A Real Tuning Session 1 class Vec { 2 double x, y; 1 2 3 sub(v) { 4 res=new Vec(x-v.x, y-v.y); 5 return res; 6 } 7 } 8 for (i = 0; i < N; i++) { 9 t = in[i+2].sub(a[i-1]); 1 10 q[i] = t; 11 t = in[i+1].sub(a[i-2]); 2 12 // use of fields of t 13 } …… 80 t=q[*]; 81 // use of fields of t 22
- 23. A Real Tuning Session 1 class Vec { 2 double x, y; 1 2 3 sub(v) { 4 res=new Vec(x-v.x, y-v.y); 5 return res; 6 } 7 } 8 for (i = 0; i < N; i++) { 9 t = in[i+2].sub(a[i-1]); 1 10 q[i] = t; 11 t = in[i+1].sub(a[i-2]); 2 12 // use of fields of t 13 } …… 80 t=q[*]; 81 // use of fields of t 23
- 24. A Real Tuning Session 1 class Vec { 1 class Vec { 2 double x, y; 2 double x, y; 3 sub(v) { 3 sub_rev(v, res) { 4 res=new Vec(x-v.x, y-v.y); 4 res.x = x-v.x; 5 return res; 5 res.y = y-v.y; 6 } 6 } 7 } tuning 7 } = new Vec; // reusable nt 8 for (i = 0; i < N; i++) { 8 for (i = 0; i < N; i++) { 9 t = in[i+2].sub(a[i-1]); 9 t = in[i+2].sub(a[i-1]); 10 q[i] = t; 10 q[i] = t; 11 t = in[i+1].sub(a[i-2]); 11 in[i+1].sub_rev(a[i-2], nt); 12 // use of fields of t 12 // use of fields of nt 13 } 13 } …… …… 80 t=q[*]; 80 t=q[*]; 81 // use of fields of t 81 // use of fields of t 24
- 25. A Real Tuning Session 1 class Vec { 1 class Vec { 2 double x, y; 2 double x, y; 3 sub(v) { 3 sub_rev(v, res) { 4 res=new Vec(x-v.x, y-v.y); 4 res.x = x-v.x; 5 return res; 5 res.y = y-v.y; 6 } 6 } 7 } tuning 7 } = new Vec; // reusable nt 8 for (i = 0; i < N; i++) { 8 for (i = 0; i < N; i++) { 9 t = in[i+2].sub(a[i-1]); 9 t = in[i+2].sub(a[i-1]); 10 q[i] = t; 10 q[i] = t; 11 t = in[i+1].sub(a[i-2]); 11 in[i+1].sub_rev(a[i-2], nt); 12 // use of fields of t 12 // use of fields of nt 13 } 13 } …… …… 80 t=q[*]; Reductions: 13% in running time and 80 t=q[*]; 81 // use of fields of t 73% in #allocated objectsof fields of t 81 // use 25
- 26. Examples of Inefficiency Pa`erns • Temporary objects for method returns – ReducKons for euler: 13% in running Kme and 73% in #allocated objects • Redundant data representaKon – mst: 63% and 40% • Unnecessary eager object creaKon – chart: 8% and 8% – jflex: 3% and 27% • Expensive specializaKon for sanity checks – bloat: 10% and 11% 26
- 27. Conclusions • Reference propagaKon profiling in Jikes RVM • Understanding reference propagaKon is a good starKng point for performance tuning • Client analyses can uncover performance inefficiencies, and lead to effecKve tuning soluKons 27
- 28. Thank you 28