By Francisco Corbera, Rafael Asenjo, Emilio Zapata (auth.), Samuel P. Midkiff, José E. Moreira, Manish Gupta, Siddhartha Chatterjee, Jeanne Ferrante, Jan Prins, William Pugh, Chau-Wen Tseng (eds.)
This quantity comprises the papers provided on the thirteenth overseas Workshop on Languages and Compilers for Parallel Computing. It additionally includes prolonged abstracts of submissions that have been permitted as posters. The workshop was once held on the IBM T. J. Watson examine middle in Yorktown Heights, long island. As in earlier years, the workshop desirous about concerns in optimizing compilers, languages, and software program environments for prime functionality computing. This keeps a development during which languages, compilers, and software program environments for prime functionality computing, and never strictly parallel computing, has been the organizing subject. As in earlier years, individuals got here from Asia, North the US, and Europe. This workshop re?ected the paintings of many folks. particularly, the individuals of the guidance committee, David Padua, Alex Nicolau, Utpal Banerjee, and David Gelernter, were instrumental in preserving the focal point and caliber of the workshop because it used to be ?rst held in 1988 in Urbana-Champaign. the help of the opposite individuals of this system committee – Larry Carter, Sid Chatterjee, Jeanne Ferrante, Jans Prins, invoice Pugh, and Chau-wen Tseng – was once the most important. The infrastructure on the IBM T. J. Watson study heart supplied undemanding logistical help. The IBM T. J. Watson learn heart additionally supplied ?nancial help through underwriting a lot of the cost of the workshop. Appreciation should also be prolonged to Marc Snir and Pratap Pattnaik of the IBM T. J. Watson examine heart for his or her support.
Read or Download Languages and Compilers for Parallel Computing: 13th International Workshop, LCPC 2000 Yorktown Heights, NY, USA, August 10–12, 2000 Revised Papers PDF
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Additional info for Languages and Compilers for Parallel Computing: 13th International Workshop, LCPC 2000 Yorktown Heights, NY, USA, August 10–12, 2000 Revised Papers
Example text
Xr ), , . . , ) ( , . . , ) = g (V ) 2. In a local computation, each processor applies a local function fi to the local data si in order to produce a new state si . The substep for the local computation does not involve the common memory, so the input and output vectors x and y are empty. In this operation, the programmer must supply the argument function f , which determines the behaviors of the sites. EXECUTE (f, . . , f ) (s1 , . . , sn ) ( ) = ((s1 , . . , sn ), ()) where (si , ) = f (si , ) V = (( ), , .
The initialization, as well as each of the log n loop iterations, requires O(1) time, so the full algorithm has time cost O(log n). 28 John O’Donnell, Thomas Rauber, and Gudula R¨ unger Initially: inputs are in X0 , . . , Xn−1 . i Result: Yi = j=0 Xj for 0 ≤ i < n procedure parScanl (⊕, n, X, Y ) READ bi := Xi for 0 ≤ i < n EXECUTE WRITE Yi := bi for 0 ≤ i < n for j := 0 to log n − 1 do READ xi := Yi−2j for 2j ≤ i < n EXECUTE bi := xi + bi for 2j ≤ i < n WRITE Yi := bi for 0 ≤ i < n READ bi := Xi is an abbreviation for READ with gi ((x0 , .
Note that the equivalence of two cost measures for an APM does not require that they yield the same cost value for each program. 3 Monotonicity of Cost Measures The cost measure for an APM can be used to guide horizontal transformations. For this purpose, a cost measure must fulfil the property that a horizontal transformation that is useful for an APM M is also useful for all concretizations of M . This property is described more precisely by the notion of monotonicity of cost measures. We consider APMs M2 and M1 where M2 is a child of M1 in the APM hierarchy.